Fire and gas hazards pose significant threats to industrial environments, necessitating the implementation of advanced detection and mitigation systems. Traditional flame and gas detectors, while effective, often suffer from limitations such as false alarms, delayed responses, and environmental interferences. Smart optimization of these detectors using artificial intelligence (AI), machine learning (ML), sensor fusion, and predictive analytics has emerged as a transformative approach to enhancing industrial safety. This paper explores various smart optimization techniques, including AI-driven classification models, real-time monitoring through the Internet of Things (IoT), and deep learning-based fire detection strategies. The study also highlights industrial applications of optimized flame and gas detection systems, evaluating their effectiveness compared to conventional models. Finally, future research directions in edge computing, digital twins, and sustainable safety technologies are discussed to provide a roadmap for further advancements in this domain.