diff --git a/src/sagemaker/tuner.py b/src/sagemaker/tuner.py
index 685927b52f..0329d60db8 100644
--- a/src/sagemaker/tuner.py
+++ b/src/sagemaker/tuner.py
@@ -594,7 +594,7 @@ def __init__(
         self,
         estimator: EstimatorBase,
         objective_metric_name: Union[str, PipelineVariable],
-        hyperparameter_ranges: Dict[str, ParameterRange],
+        hyperparameter_ranges: Dict[Union[str, PipelineVariable], ParameterRange],
         metric_definitions: Optional[List[Dict[str, Union[str, PipelineVariable]]]] = None,
         strategy: Union[str, PipelineVariable] = "Bayesian",
         objective_type: Union[str, PipelineVariable] = "Maximize",
@@ -624,10 +624,11 @@ def __init__(
                 instance.
             objective_metric_name (str or PipelineVariable): Name of the metric for evaluating
                 training jobs.
-            hyperparameter_ranges (dict[str, sagemaker.parameter.ParameterRange]): Dictionary of
-                parameter ranges. These parameter ranges can be one
+            hyperparameter_ranges (dict[str or PipelineVariable, sagemaker.parameter.ParameterRange]):
+                Dictionary of parameter ranges. These parameter ranges can be one
                 of three types: Continuous, Integer, or Categorical. The keys of
-                the dictionary are the names of the hyperparameter, and the
+                the dictionary are the names of the hyperparameter (a str, or a
+                PipelineVariable such as a pipeline ParameterString), and the
                 values are the appropriate parameter range class to represent
                 the range.
             metric_definitions (list[dict[str, str] or list[dict[str, PipelineVariable]]): A list of
@@ -1906,8 +1907,9 @@ def create(
                 names as in estimator_dict, and there must be one entry for each estimator in
                 estimator_dict. Each value is a dictionary of sagemaker.parameter.ParameterRange
                 instance, which can be one of three types: Continuous, Integer, or Categorical.
-                The keys of each ParameterRange dictionaries are the names of the hyperparameter,
-                and the values are the appropriate parameter range class to represent the range.
+                The keys of each ParameterRange dictionary are the names of the hyperparameter
+                (a str, or a PipelineVariable such as a pipeline ParameterString), and the values
+                are the appropriate parameter range class to represent the range.
             metric_definitions_dict (dict(str, list[dict]]): Dictionary of metric definitions.
                 The keys are the same set or a subset of estimator names as in estimator_dict,
                 and there must be one entry for each estimator in estimator_dict. Each value is
diff --git a/tests/unit/test_tuner.py b/tests/unit/test_tuner.py
index 9b9e34f95f..6a6dbcbbd0 100644
--- a/tests/unit/test_tuner.py
+++ b/tests/unit/test_tuner.py
@@ -15,6 +15,7 @@
 import copy
 import os
 import re
+import typing
 
 import pytest
 from mock import Mock, patch
@@ -42,6 +43,7 @@
     create_transfer_learning_tuner,
     HyperparameterTuner,
 )
+from sagemaker.workflow.entities import PipelineVariable
 from sagemaker.workflow.functions import JsonGet, Join
 from sagemaker.workflow.parameters import ParameterString, ParameterInteger
 
@@ -2230,3 +2232,41 @@ def test_create_tuner_with_grid_search_strategy():
 
     assert tuner is not None
     assert tuner.max_jobs is None
+
+
+def test_hyperparameter_ranges_annotation_allows_pipeline_variable_keys():
+    """Regression for #5243.
+
+    The ``hyperparameter_ranges`` key type must allow PipelineVariable (e.g. a pipeline
+    ParameterString), not only ``str``, since the tuner accepts pipeline variables as
+    hyperparameter names. This asserts the resolved annotation, so it fails if the type
+    is narrowed back to ``Dict[str, ParameterRange]``.
+    """
+    annotation = HyperparameterTuner.__init__.__annotations__["hyperparameter_ranges"]
+
+    key_type, value_type = typing.get_args(annotation)
+    key_options = typing.get_args(key_type)  # (str, PipelineVariable)
+
+    assert str in key_options, f"str must remain a valid key type, got {key_options}"
+    assert (
+        PipelineVariable in key_options
+    ), f"PipelineVariable must be an allowed key type, got {key_options}"
+    assert value_type is ParameterRange
+
+
+def test_init_with_pipeline_variable_hyperparameter_key(estimator):
+    """A pipeline ParameterString used as a hyperparameter-range key is accepted (#5243).
+
+    NOTE: this is a runtime sanity check, not the regression guard -- annotations are not
+    enforced at runtime, so this passes with or without the fix. The guard against
+    re-narrowing the type is test_hyperparameter_ranges_annotation_allows_pipeline_variable_keys.
+    """
+    hparam_name = ParameterString(name="HParamName", default_value="hparam")
+
+    tuner = HyperparameterTuner(
+        estimator=estimator,
+        objective_metric_name=OBJECTIVE_METRIC_NAME,
+        hyperparameter_ranges={hparam_name: CategoricalParameter([1, 2])},
+    )
+
+    assert hparam_name in tuner._hyperparameter_ranges