diff --git a/pkgs/swarmauri/swarmauri/measurements/concrete/UniquenessMeasurement.py b/pkgs/swarmauri/swarmauri/measurements/concrete/UniquenessMeasurement.py
new file mode 100644
index 000000000..f807d86fd
--- /dev/null
+++ b/pkgs/swarmauri/swarmauri/measurements/concrete/UniquenessMeasurement.py
@@ -0,0 +1,96 @@
+from typing import Any, Dict, List, Literal, Union
+import pandas as pd
+from swarmauri.measurements.base.MeasurementBase import MeasurementBase
+
+
+class UniquenessMeasurement(MeasurementBase):
+    """
+    Measurement for evaluating the uniqueness of values in a dataset.
+    Uniqueness is calculated as the percentage of distinct values relative to the total number of values.
+
+    Attributes:
+        type (Literal['UniquenessMeasurement']): Type identifier for the measurement
+        unit (str): Unit of measurement (percentage)
+        value (float): Stores the calculated uniqueness score
+    """
+
+    type: Literal["UniquenessMeasurement"] = "UniquenessMeasurement"
+    unit: str = "%"  # Percentage as the unit of measurement
+
+    def calculate_uniqueness(self, data: Union[pd.DataFrame, List, Dict]) -> float:
+        """
+        Calculates the uniqueness score for different data types.
+
+        Args:
+            data: Input data which can be a pandas DataFrame, List, or Dictionary
+
+        Returns:
+            float: Uniqueness score as a percentage (0-100)
+
+        Raises:
+            ValueError: If the input data type is not supported
+        """
+        if isinstance(data, pd.DataFrame):
+            if data.empty:
+                return 0.0
+            # For DataFrame, calculate uniqueness across all columns
+            total_values = data.size
+            unique_values = sum(data[col].nunique() for col in data.columns)
+            return (unique_values / total_values) * 100
+
+        elif isinstance(data, list):
+            if not data:
+                return 0.0
+            total_values = len(data)
+            unique_values = len(
+                set(str(x) for x in data)
+            )  # Convert to strings to handle unhashable types
+            return (unique_values / total_values) * 100
+
+        elif isinstance(data, dict):
+            if not data:
+                return 0.0
+            total_values = len(data)
+            unique_values = len(
+                set(str(v) for v in data.values())
+            )  # Convert to strings to handle unhashable types
+            return (unique_values / total_values) * 100
+
+        else:
+            raise ValueError(
+                "Unsupported data type. Please provide DataFrame, List, or Dict."
+            )
+
+    def call(
+        self, data: Union[pd.DataFrame, List, Dict], kwargs: Dict[str, Any] = None
+    ) -> float:
+        """
+        Calculates and returns the uniqueness score for the provided data.
+
+        Args:
+            data: Input data to evaluate uniqueness
+            kwargs: Additional parameters (reserved for future use)
+
+        Returns:
+            float: Uniqueness score as a percentage (0-100)
+        """
+        self.value = self.calculate_uniqueness(data)
+        return self.value
+
+    def get_column_uniqueness(self, df: pd.DataFrame) -> Dict[str, float]:
+        """
+        Calculate uniqueness scores for individual columns in a DataFrame.
+
+        Args:
+            df: Input DataFrame
+
+        Returns:
+            Dict[str, float]: Dictionary mapping column names to their uniqueness scores
+
+        Raises:
+            ValueError: If input is not a pandas DataFrame
+        """
+        if not isinstance(df, pd.DataFrame):
+            raise ValueError("Input must be a pandas DataFrame")
+
+        return {column: (df[column].nunique() / len(df) * 100) for column in df.columns}
diff --git a/pkgs/swarmauri/tests/unit/measurements/UniquenessMeasurement_unit_test.py b/pkgs/swarmauri/tests/unit/measurements/UniquenessMeasurement_unit_test.py
new file mode 100644
index 000000000..690002cc8
--- /dev/null
+++ b/pkgs/swarmauri/tests/unit/measurements/UniquenessMeasurement_unit_test.py
@@ -0,0 +1,83 @@
+import pytest
+import pandas as pd
+from swarmauri.measurements.concrete.UniquenessMeasurement import (
+    UniquenessMeasurement as Measurement,
+)
+
+
+@pytest.mark.unit
+def test_ubc_resource():
+    assert Measurement(unit="%").resource == "Measurement"
+
+
+@pytest.mark.unit
+def test_ubc_type():
+    measurement = Measurement(unit="%")
+    assert measurement.type == "UniquenessMeasurement"
+
+
+@pytest.mark.unit
+def test_serialization():
+    measurement = Measurement(unit="%", value=75.0)
+    assert (
+        measurement.id
+        == Measurement.model_validate_json(measurement.model_dump_json()).id
+    )
+
+
+@pytest.mark.unit
+def test_measurement_value():
+    measurement = Measurement(unit="%")
+    test_data = ["A", "B", "A", "C", "B", "D"]  # 4 unique values out of 6 total
+    result = measurement.call(test_data)
+    assert result == pytest.approx(
+        66.66666666666667, rel=1e-9
+    )  # Using approx for float comparison
+    assert measurement.value == pytest.approx(66.66666666666667, rel=1e-9)
+
+
+@pytest.mark.unit
+def test_measurement_unit():
+    measurement = Measurement(unit="%")
+    test_data = ["A", "B", "A", "C"]
+    measurement.call(test_data)
+    assert measurement.unit == "%"
+
+
+@pytest.mark.unit
+def test_dataframe_uniqueness():
+    measurement = Measurement(unit="%")
+    df = pd.DataFrame({"col1": [1, 2, 2, 3], "col2": ["A", "A", "B", "C"]})
+    result = measurement.call(df)
+    assert result == pytest.approx(75.0)
+
+
+@pytest.mark.unit
+def test_column_uniqueness():
+    measurement = Measurement(unit="%")
+    df = pd.DataFrame({"col1": [1, 2, 2, 3], "col2": ["A", "A", "B", "C"]})
+    column_uniqueness = measurement.get_column_uniqueness(df)
+    assert column_uniqueness["col1"] == pytest.approx(75.0)
+    assert column_uniqueness["col2"] == pytest.approx(75.0)
+
+
+@pytest.mark.unit
+def test_empty_input():
+    measurement = Measurement(unit="%")
+    assert measurement.call([]) == 0.0
+    assert measurement.call({}) == 0.0
+    assert measurement.call(pd.DataFrame()) == 0.0
+
+
+@pytest.mark.unit
+def test_dict_uniqueness():
+    measurement = Measurement(unit="%")
+    test_dict = {"a": 1, "b": 2, "c": 1, "d": 3}  # 3 unique values out of 4
+    assert measurement.call(test_dict) == pytest.approx(75.0)
+
+
+@pytest.mark.unit
+def test_invalid_input():
+    measurement = Measurement(unit="%")
+    with pytest.raises(ValueError):
+        measurement.call(42)  # Invalid input type