You can just "call" your model with an array of the correct shape:
model(np.array([[6.7, 3.3, 5.7, 2.5]]))
Full example:
from sklearn.datasets import load_iris
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
import numpy as np
X, y = load_iris(return_X_y=True)
model = Sequential([
Dense(16, activation='relu'),
Dense(32, activation='relu'),
Dense(1)])
model.compile(loss='mean_absolute_error', optimizer='adam')
history = model.fit(X, y, epochs=10, verbose=0)
print(model(np.array([[6.7, 3.3, 5.7, 2.5]])))
<tf.Tensor: shape=(1, 1), dtype=float64, numpy=array([[1.92517677]])>