Identifying and classifying twins pose challenges, yet hold significant applications in medical research, forensic science, and social science. In recent years, machine learning has emerged as a promising approach to twins identification and classification. Leveraging the unparalleled genetic similarity of identical twins, it is unsurprising that their fingerprints exhibit the highest degree of resemblance, rendering DNA ineffective for discrimination. Face recognition, a burgeoning field of image analysis and understanding, has garnered significant interest, especially in recent years. This study delves into the potential of both traditional decision trees and cutting-edge Convolutional Neural Networks (CNNs) within machine learning (ML) for accurate twin identification and classification. Key metrics like specificity, sensitivity, and accuracy are employed to gauge performance. The results unveil the superiority of CNNs over decision trees in every aspect, achieving exceptional accuracy in twin recognition. MATLAB development tool will be used to demonstrate the. The ultimate objective is to establish a robust system that capitalizes on inherent characteristics to distinguish between individual twins with unparalleled precision.