Chloride Stress Corrosion Cracking (Cl-SCC) remains a critical degradation challenge for stainless steel assets in offshore and process industries. Conventional inspection and Risk-Based Inspection (RBI) methods often detect damage only after initiation and rely on static assumptions, limiting early intervention. This paper contributes an AI-driven framework for Chloride stress corrosion cracking risk management that (i) integrates sensor data from key variables such as stress, temperature, and chloride concentration,(ii) compares and benchmarks three machine learning models on an experimentally validated corrosion dataset (iii) applies explainable AI (SHAP) that provides interpretability by identifying critical features influencing corrosion which supports auditable results,(iv) outlines a pragmatic approach to embed predictive analytics into inspection planning based on RBI. The proposed approach enables proactive monitoring, adaptive risk estimation, and optimized inspection planning, offering a scalable pathway for predictive corrosion management in industrial environments.