This work explores the successful integration of machine learning and cryptography, wherein distinguishers have been designed with machine learning techniques. The primary focus is how effectively the deep learning models can be leveraged to uncover intricate patterns in data. Three Neural Distinguishers have been proposed using LightGBM, CNN, and LSTM algorithms to analyze Lightweight Block Ciphers. These models have a simpler architecture than most other related models previously used in the literature. The proposed methods have been applied to the ciphers, LEA, PRESENT, Piccolo-80, and MIDORI. This has led us to achieve an improved distinguisher for LEA covering 14 rounds and PRESENT covering 16 rounds. For the first time, new distinguishers have been achieved for Piccolo-80, covering 9 rounds. A new idea of employing deep learning-aided related key and weak key attacks has been used to obtain a distinguisher covering full rounds of MIDORI.