The research highlights the evolution of well logging from manual to intelligent systems, emphasizing the role of AI in enhancing lithology identification from rock cutting images. By adopting deep learning models, the study aims to transition well logging into a more automated and accurate phase, reducing reliance on manual spectrum analysis and empirical methods that previously limited efficiency and precision. The proposed methodology involves processing rock cutting images with advanced deep learning techniques to identify lithological characteristics efficiently. Experimental results demonstrate that the application of convolutional neural networks, particularly the Residual Network (ResNet) model, significantly improves the accuracy and reliability of rock type identification, supporting the feasibility of AI in practical field applications. This study not only contributes to the technological advancement of well logging but also ensures better decision-making in drilling operations by providing timely and accurate geological data. The findings underscore the potential of integrating AI with traditional geotechnical engineering practices, promising significant improvements in exploration and development activities.