In this study, we develop a comprehensive Fire Risk Assessment with a Machine Learning Environment (FRAME v1.0) for tropical fragmented forest systems by adding fuel availability and anthropogenic ignition factors to a well-known climate-driven fire hazard assessment model. Our work focuses on the forests of India, a representative example of tropical fragmented forest systems in a densely populated country where fire behavior is complex and influenced strongly by natural and human factors. In this work, we first developed a Fire Danger Rating System (FDRS) based on the Fire Weather Index (FWI) module of the Canadian Forest Fire Danger Rating System (CFFDRS) and machine learning (ML) techniques. The integration of ML techniques increased the FDRS's ability to estimate fire probability by 30–50 %. While the FDRS forms the core meteorological component of FRAME v1.0, it does not account for other critical drivers. Hence, we extended this FDRS to a comprehensive fire risk assessment framework by incorporating fuel availability and anthropogenic ignition factors using machine learning predictive algorithms with fire count as the target variable. We observed that the neural network-based model performed best among all algorithms across different forest zones of India. Maximum relevance minimum redundancy analysis revealed spatial heterogeneity in dominant fire drivers, although weather remained a consistently critical factor. FRAME v1.0 provides a scalable operational foundation for fire risk assessment in tropical fragmented forests and demonstrates how machine learning can enhance physically grounded fire danger systems.