Evaluating shale maturity is essential for assessing unconventional hydrocarbon resource potential and development value. Conventional evaluation methods depend on geochemical laboratory analysis, which faces challenges including limited sampling points, time-consuming processes, and high expenses. The advancement of imaging logging technologies has provided new data sources for continuous and rapid shale maturity assessment. This study introduces an artificial intelligence-based approach for determining shale maturity using imaging log data. The methodology begins with selecting key imaging log parameters and establishing training datasets that incorporate geological and geochemical information. Various machine learning techniques (including support vector machines, random forests, and deep neural networks) are then employed to model complex relationships between log responses and maturity levels. Testing on field data demonstrates that this approach enhances both accuracy and efficiency in maturity identification. This research offers a smart, efficient alternative for maturity evaluation in shale resource exploration and development, showing considerable practical potential.