In recent years, ML based differential distinguishers have been explored and compared with the classical methods. Complexity of a key recovery attack on block ciphers is calculated using the probability of a differential distinguisher provided by classical methods. Since theoretical computations suffice to calculate the data complexity in these cases, so there seems no restrictions on the practical availability of computational resources to attack a block cipher using classical methods. However, ML based differential cryptanalysis is based on the machine learning model that uses encrypted data to learn its features using available compute power. This poses a restriction on the accuracy of ML distinguisher for increased number of rounds and ciphers with large block size. Moreover, we can still construct the distinguisher but the accuracy becomes very low in such cases. In this paper, we present a new approach to construct the differential distinguisher with high accuracy using the existing ML based distinguisher of low accuracy. This approach outperforms all existing approaches with similar objective. We demonstrate our method to construct the high accuracy ML based distinguishers for GIFT-128 and ASCON permutation. For GIFT-128, accuracy of 7-round distinguisher is increased to 98.8% with \(2^{9}\) data complexity. For ASCON, accuracy of 4-round distinguisher is increased to 99.4% with \(2^{18}\) data complexity. We also construct the ML based differential distinguisher that uses only a few bits of the output for training and prediction. A 7-round distinguisher of 98.7% accuracy is constructed for GIFT-128 with \(2^{14}\) data complexity that uses only 16 bits for training and prediction. For ASCON, a 4-round distinguisher of 98.7% accuracy is constructed with data complexity \(2^{18}\) using only 40 bits. We present the first ML based distinguisher for 8 rounds of GIFT-128 using the differential-ML distinguisher presented in Latincrypt-2021. This distinguisher is constructed with 99.8% accuracy and \(2^{18}\) data complexity.