diff --git a/python/tvm/relay/frontend/tflite.py b/python/tvm/relay/frontend/tflite.py
index db6e053628bf..4d607e46c97f 100644
--- a/python/tvm/relay/frontend/tflite.py
+++ b/python/tvm/relay/frontend/tflite.py
@@ -91,6 +91,7 @@ def __init__(self, model, subgraph, exp_tab):
             "EQUAL": self.convert_equal,
             "EXP": self.convert_exp,
             "EXPAND_DIMS": self.convert_expand_dims,
+            "FAKE_QUANT": self.convert_fake_quant,
             "FILL": self.convert_fill,
             "FLOOR_DIV": self.convert_floor_div,
             "FLOOR_MOD": self.convert_floor_mod,
@@ -3336,6 +3337,56 @@ def convert_densify(self, op):
 
         self.set_prefetched_node(output_tensor.tensor_idx, dense_weight)
 
+    def convert_fake_quant(self, op):
+        """Convert TFLite FAKE_QUANT"""
+        input_tensors = self.get_input_tensors(op)
+        assert len(input_tensors) == 1, "input tensors length should be 1"
+
+        input_tensor = input_tensors[0]
+        in_expr = self.get_expr(input_tensor.tensor_idx)
+
+        from tflite.BuiltinOptions import BuiltinOptions
+        from tflite.FakeQuantOptions import FakeQuantOptions
+
+        assert op.BuiltinOptionsType() == BuiltinOptions.FakeQuantOptions
+
+        op_options = op.BuiltinOptions()
+        fake_quant_options = FakeQuantOptions()
+        fake_quant_options.Init(op_options.Bytes, op_options.Pos)
+
+        opt_min = fake_quant_options.Min()
+        opt_max = fake_quant_options.Max()
+        narrow_range = fake_quant_options.NarrowRange()
+        num_bits = fake_quant_options.NumBits()
+
+        assert 2 <= num_bits <= 16
+
+        quant_min = 1 if narrow_range else 0
+        quant_max = (1 << num_bits) - 1
+        scale = (opt_max - opt_min) / (quant_max - quant_min)
+
+        zero_point_from_min = quant_min - opt_min / scale
+        if zero_point_from_min <= quant_min:
+            nudged_zero_point = quant_min
+        elif zero_point_from_min >= quant_max:
+            nudged_zero_point = quant_max
+        else:
+            nudged_zero_point = round(zero_point_from_min)
+
+        nudged_min = (quant_min - nudged_zero_point) * scale
+        nudged_max = (quant_max - nudged_zero_point) * scale
+
+        nudged_min_expr = _op.const(nudged_min)
+        clamped = _op.clip(in_expr, nudged_min, nudged_max)
+        clamped_shifted = _op.subtract(clamped, nudged_min_expr)
+
+        half = _op.const(0.5)
+        one = _op.const(1.0)
+        scale_expr = _op.const(scale)
+        inv_scale = _op.divide(one, scale_expr)
+        rounded = _op.floor(_op.add(_op.multiply(clamped_shifted, inv_scale), half))
+        return _op.add(_op.multiply(rounded, scale_expr), nudged_min_expr)
+
     def get_expr(self, input_tensor_idx):
         return self.exp_tab.get_expr(get_tensor_name(self.subgraph, input_tensor_idx))
 
diff --git a/tests/python/frontend/tflite/test_forward.py b/tests/python/frontend/tflite/test_forward.py
index 7b7f1b1c43b8..f2941030f0ab 100644
--- a/tests/python/frontend/tflite/test_forward.py
+++ b/tests/python/frontend/tflite/test_forward.py
@@ -322,7 +322,6 @@ def compare_tflite_with_tvm(
                 out_names=out_names,
                 mode=mode,
             )
-
             # WARNING: the results could well be random values clipped to 0 or 255 because of badly tuned output
             # range for the specific operator. While adding test ensure that we aren't getting only clipped values
             # in output tensors that still pass the assertion. For reference see _test_elemwise_qnn_out_range()
@@ -2618,6 +2617,22 @@ def test_forward_select():
             )
 
 
+@pytest.mark.parametrize("quant_bits", [2, 4, 8, 16])
+@pytest.mark.parametrize(
+    "value, min, max", [[-10.11, -6, 6], [-3.55, -6, 6], [0, -6, 6], [3.55, -6, 6], [10.11, -6, 6]]
+)
+def test_forward_fake_quant(value, min, max, quant_bits):
+    with tf.Graph().as_default():
+        with tf.Session() as sess:
+            input = tf.placeholder(tf.float32, shape=[1], name="input")
+            out = tf.quantization.fake_quant_with_min_max_args(
+                input, min=min, max=max, num_bits=quant_bits, name=None
+            )
+
+            in_data = np.float32(value)
+            compare_tflite_with_tvm([in_data], ["input:0"], [input], [out])
+
+
 # Squeeze
 # -------