Formal methods for safety-critical machine learning: a systematic literature review

Analysis reveals persistent challenges and gaps, including scalability to large and complex models, integration with training processes, and limited real-world validation. Future research opportunities include developing integrated training-verification loops, scalable verification frameworks, hybrid formal methods, and novel techniques for emerging ML paradigms such as Large Language Models. This work serves both as a state-of-the-art reference and as a roadmap for advancing the safe deployment of ML systems.