The global transition to sustainable energy systems has positioned hydrogen as a critical energy carrier. While its benefits, such as high energy density and compatibility with renewables, are clear, integrating hydrogen into existing infrastructure poses significant safety and reliability challenges, particularly related to material degradation mechanisms like hydrogen embrittlement and hydrogen-enhanced fatigue. Traditional risk-based inspection (RBI) methodologies, which optimize maintenance efforts based on component risk levels, fall short in addressing the complexities introduced by hydrogen environments. This chapter introduces HyRIS (Risk-Based Inspection Strategy for Hydrogen Technologies), an innovative framework that integrates artificial intelligence (AI) with conventional RBI practices to address hydrogen-induced degradation mechanisms. By leveraging machine learning models, HyRIS predicts the probability and severity of fatigue-induced failures, enabling precise risk assessments and tailored inspection strategies. The chapter details the methodology’s principles, its applications to hydrogen pipeline integrity, and its broader implications for ensuring the safe and reliable operation of hydrogen infrastructure. The integration of AI with RBI frameworks represents a transformative approach to managing the risks associated with hydrogen, supporting its role in the energy transition.