diff --git a/dask_sql/context.py b/dask_sql/context.py
index ab0c2ae71..19bba68ae 100644
--- a/dask_sql/context.py
+++ b/dask_sql/context.py
@@ -829,7 +829,8 @@ def _get_ral(self, sql):
         # Optimize the `LogicalPlan` or skip if configured
         if dask_config.get("sql.optimize"):
             try:
-                rel = self.context.optimize_relational_algebra(nonOptimizedRel)
+                rel = self.context.run_preoptimizer(nonOptimizedRel)
+                rel = self.context.optimize_relational_algebra(rel)
             except DFOptimizationException as oe:
                 # Use original plan and warn about inability to optimize plan
                 rel = nonOptimizedRel
diff --git a/src/sql.rs b/src/sql.rs
index c9a600225..4d7908eee 100644
--- a/src/sql.rs
+++ b/src/sql.rs
@@ -4,6 +4,7 @@ pub mod function;
 pub mod logical;
 pub mod optimizer;
 pub mod parser_utils;
+pub mod preoptimizer;
 pub mod schema;
 pub mod statement;
 pub mod table;
@@ -70,6 +71,7 @@ use crate::{
             show_tables::ShowTablesPlanNode,
             PyLogicalPlan,
         },
+        preoptimizer::datetime_coercion,
     },
 };
 
@@ -559,6 +561,17 @@ impl DaskSQLContext {
             .map_err(py_parsing_exp)
     }
 
+    pub fn run_preoptimizer(
+        &self,
+        existing_plan: logical::PyLogicalPlan,
+    ) -> PyResult<logical::PyLogicalPlan> {
+        if let Some(plan) = datetime_coercion(&existing_plan.original_plan) {
+            Ok(plan.into())
+        } else {
+            Ok(existing_plan)
+        }
+    }
+
     /// Accepts an existing relational plan, `LogicalPlan`, and optimizes it
     /// by applying a set of `optimizer` trait implementations against the
     /// `LogicalPlan`
diff --git a/src/sql/preoptimizer.rs b/src/sql/preoptimizer.rs
new file mode 100644
index 000000000..6d021c737
--- /dev/null
+++ b/src/sql/preoptimizer.rs
@@ -0,0 +1,205 @@
+use std::collections::HashMap;
+
+use datafusion_python::{
+    datafusion::arrow::datatypes::{DataType, TimeUnit},
+    datafusion_common::{Column, DFField, ScalarValue},
+    datafusion_expr::{logical_plan::Filter, BinaryExpr, Expr, LogicalPlan, Operator},
+};
+
+// Sometimes, DataFusion's optimizer will raise an OptimizationException before we even get the
+// chance to correct it anywhere. In these cases, we can still modify the LogicalPlan as an
+// optimizer rule would, however we have to run it independently, separately of DataFusion's
+// optimization framework. Ideally, these "pre-optimization" rules aren't performing any complex
+// logic, but rather "pre-processing" the LogicalPlan for the optimizer. For example, the
+// datetime_coercion preoptimizer rule fixes a bug involving Timestamp-Int operations.
+
+// Helper function for datetime_coercion rule, which returns a vector of columns and literals
+// involved in a (possibly nested) BinaryExpr mathematical expression, and the mathematical
+// BinaryExpr itself
+fn extract_columns_and_literals(expr: &Expr) -> Vec<(Vec<Expr>, Expr)> {
+    let mut result = Vec::new();
+    if let Expr::BinaryExpr(b) = expr {
+        let left = *b.left.clone();
+        let right = *b.right.clone();
+        if let Operator::Plus
+        | Operator::Minus
+        | Operator::Multiply
+        | Operator::Divide
+        | Operator::Modulo = &b.op
+        {
+            let mut operands = Vec::new();
+            if let Expr::Column(_) | Expr::Literal(_) = left.clone() {
+                operands.push(left);
+            } else {
+                let vector_of_vectors = extract_columns_and_literals(&left);
+                let mut flattened = Vec::new();
+                for vector in vector_of_vectors {
+                    flattened.extend(vector.0);
+                }
+                operands.append(&mut flattened);
+            }
+
+            if let Expr::Column(_) | Expr::Literal(_) = right.clone() {
+                operands.push(right);
+            } else {
+                let vector_of_vectors = extract_columns_and_literals(&right);
+                let mut flattened = Vec::new();
+                for vector in vector_of_vectors {
+                    flattened.extend(vector.0);
+                }
+                operands.append(&mut flattened);
+            }
+
+            result.push((operands, expr.clone()));
+        } else {
+            if let Expr::BinaryExpr(_) = left {
+                result.append(&mut extract_columns_and_literals(&left));
+            }
+
+            if let Expr::BinaryExpr(_) = right {
+                result.append(&mut extract_columns_and_literals(&right));
+            }
+        }
+    }
+    result
+}
+
+// Helper function for datetime_coercion rule, which uses a LogicalPlan's schema to obtain the
+// datatype of a desired column
+fn find_data_type(column: Column, fields: Vec<DFField>) -> Option<DataType> {
+    for field in fields {
+        if let Some(qualifier) = field.qualifier() {
+            if column.relation.is_some()
+                && qualifier.table() == column.relation.clone().unwrap().table()
+                && field.field().name() == &column.name
+            {
+                return Some(field.field().data_type().clone());
+            }
+        }
+    }
+    None
+}
+
+// Helper function for datetime_coercion rule, which, given a BinaryExpr and a HashMap in which the
+// key represents a Literal and the value represents a Literal to replace the key with, returns the
+// modified BinaryExpr
+fn replace_literals(expr: Expr, replacements: HashMap<Expr, Expr>) -> Expr {
+    match expr {
+        Expr::Literal(l) => {
+            if let Some(new_literal) = replacements.get(&Expr::Literal(l.clone())) {
+                new_literal.clone()
+            } else {
+                Expr::Literal(l)
+            }
+        }
+        Expr::BinaryExpr(b) => {
+            let left = replace_literals(*b.left, replacements.clone());
+            let right = replace_literals(*b.right, replacements);
+            Expr::BinaryExpr(BinaryExpr {
+                left: Box::new(left),
+                op: b.op,
+                right: Box::new(right),
+            })
+        }
+        _ => expr,
+    }
+}
+
+// Helper function for datetime_coercion rule, which, given a BinaryExpr expr and a HashMap in
+// which the key represents a BinaryExpr and the value represents a BinaryExpr to replace the key
+// with, returns the modified expr
+fn replace_binary_exprs(expr: Expr, replacements: HashMap<Expr, Expr>) -> Expr {
+    match expr {
+        Expr::BinaryExpr(b) => {
+            if let Some(new_expr) = replacements.get(&Expr::BinaryExpr(b.clone())) {
+                new_expr.clone()
+            } else {
+                let left = replace_binary_exprs(*b.left, replacements.clone());
+                let right = replace_binary_exprs(*b.right, replacements);
+                Expr::BinaryExpr(BinaryExpr {
+                    left: Box::new(left),
+                    op: b.op,
+                    right: Box::new(right),
+                })
+            }
+        }
+        _ => expr,
+    }
+}
+
+// Preoptimization rule which detects when the user is trying to perform a binary operation on a
+// datetime and an integer, then converts the integer to a IntervalMonthDayNano. For example, if we
+// have a date_col + 5, we assume that we are adding 5 days to the date_col
+pub fn datetime_coercion(plan: &LogicalPlan) -> Option<LogicalPlan> {
+    match plan {
+        LogicalPlan::Filter(f) => {
+            let filter_expr = f.predicate.clone();
+            let columns_and_literals = extract_columns_and_literals(&filter_expr);
+
+            let mut days_to_nanoseconds: Vec<(Expr, HashMap<Expr, Expr>)> = Vec::new();
+            for vector in columns_and_literals.iter() {
+                // Detect whether a timestamp is involved in the operation
+                let mut is_timestamp_operation = false;
+                for item in vector.0.iter() {
+                    if let Expr::Column(column) = item {
+                        if let Some(DataType::Timestamp(TimeUnit::Nanosecond, _)) =
+                            find_data_type(column.clone(), plan.schema().fields().clone())
+                        {
+                            is_timestamp_operation = true;
+                        }
+                    }
+                }
+
+                // Convert an integer to an IntervalMonthDayNano
+                if is_timestamp_operation {
+                    let mut find_replace = HashMap::new();
+                    for item in vector.0.iter() {
+                        if let Expr::Literal(ScalarValue::Int64(i)) = item {
+                            let ns = i.unwrap() as i128 * 18446744073709552000;
+
+                            find_replace.insert(
+                                Expr::Literal(ScalarValue::Int64(*i)),
+                                Expr::Literal(ScalarValue::IntervalMonthDayNano(Some(ns))),
+                            );
+                        }
+                    }
+                    days_to_nanoseconds.push((vector.1.clone(), find_replace));
+                }
+            }
+
+            let mut binary_exprs = HashMap::new();
+            for replacements in days_to_nanoseconds.iter() {
+                binary_exprs.insert(
+                    replacements.0.clone(),
+                    replace_literals(replacements.0.clone(), replacements.1.clone()),
+                );
+            }
+            let new_filter = replace_binary_exprs(filter_expr, binary_exprs);
+            Some(LogicalPlan::Filter(
+                Filter::try_new(new_filter, f.input.clone()).unwrap(),
+            ))
+        }
+        _ => optimize_children(plan.clone()),
+    }
+}
+
+// Function used to iterate through a LogicalPlan and update it accordingly
+fn optimize_children(existing_plan: LogicalPlan) -> Option<LogicalPlan> {
+    let plan = existing_plan.clone();
+    let new_exprs = plan.expressions();
+    let mut new_inputs = Vec::with_capacity(plan.inputs().len());
+    let mut plan_is_changed = false;
+    for input in plan.inputs() {
+        // Since datetime_coercion is the only preoptimizer rule that we have at the moment, we
+        // hardcode it here. If additional preoptimizer rules are added in the future, this can be
+        // modified
+        let new_input = datetime_coercion(input);
+        plan_is_changed = plan_is_changed || new_input.is_some();
+        new_inputs.push(new_input.unwrap_or_else(|| input.clone()))
+    }
+    if plan_is_changed {
+        Some(plan.with_new_exprs(new_exprs, &new_inputs).ok()?)
+    } else {
+        Some(existing_plan)
+    }
+}
diff --git a/tests/integration/test_rex.py b/tests/integration/test_rex.py
index e099a3ddb..e262bac96 100644
--- a/tests/integration/test_rex.py
+++ b/tests/integration/test_rex.py
@@ -1236,3 +1236,34 @@ def test_scalar_timestamps(c, gpu):
         f"SELECT TIMESTAMPDIFF(DAY, CAST({scalar1} AS TIMESTAMP), CAST({scalar2} AS TIMESTAMP)) AS dt"
     )
     assert_eq(df3, expected_df)
+
+
+def test_datetime_coercion(c):
+    d_table = pd.DataFrame(
+        {
+            "d_date": [
+                datetime(2023, 7, 1),
+                datetime(2023, 7, 5),
+                datetime(2023, 7, 10),
+                datetime(2023, 7, 15),
+            ],
+            "x": [1, 2, 3, 4],
+        }
+    )
+    c.create_table("d_table", d_table)
+
+    df = c.sql(
+        """
+        SELECT * FROM d_table d1, d_table d2
+        WHERE d2.x < d1.x + (1 + 2)
+        AND d2.d_date > d1.d_date + (2 + 3)
+    """
+    )
+    expected_df = c.sql(
+        """
+        SELECT * FROM d_table d1, d_table d2
+        WHERE d2.x < d1.x + (1 + 2)
+        AND d2.d_date > d1.d_date + INTERVAL '5 days'
+    """
+    )
+    assert_eq(df, expected_df)