Machine learning has great potential in lithology identification. Through supervised learning, unsupervised learning, semi-supervised learning, deep learning and other methods, features can be automatically extracted from complex seismic data and logging data to achieve efficient and accurate lithology classification. These methods not only improve the accuracy and efficiency of lithology identification, but also reduce the workload of geologists, allowing them to focus on higher-level analysis and decision making. Despite significant progress, machine learning still faces many challenges in lithology identification. First, data quality and quantity limitations remain a major problem, especially in certain areas where obtaining high-quality seismic and logging data is difficult. Second, the training and interpretability of complex models also need to be addressed, especially because the "black box" nature of deep learning models makes it difficult for geologists to understand their internal mechanisms and predictions. In addition, the generalization ability and overfitting of models, as well as the need for real-time data processing in practical applications, are also urgent challenges to be solved. To address these challenges, this paper proposes several solutions and future directions. Data enhancement and synthesis techniques can extend existing data sets and improve the robustness and accuracy of models. The development of interpretative models and visualization tools helps geologists understand and trust the decision-making process of models. Multi-source data fusion technology can effectively use multi-source heterogeneous data such as seismic data, well logging data and geological map to improve model performance. Online learning and transfer learning technologies can update models in real time, improve the adaptability and generalization ability of models, and develop more accurate and interpretable models.