Tuna fisheries in the Arabian Sea and Western Indian Ocean are vital for regional economies and global food security, requiring advanced tools for sustainable management. This study introduces a novel framework for Potential Fishing Zone (PFZ) identification by integrating multi-sensor remote sensing data with machine learning. A Random Forest model was developed using eight years (2014–2021) of satellite-derived oceanographic variables—sea surface temperature, salinity, chlorophyll-a, and current velocities—alongside in-situ fisheries data from Oman's Exclusive Economic Zone. The model achieved perfect classification in cross-validation and 97% accuracy on test data. Thermohaline parameters dominated predictions, with sea surface temperature at 10m depth and surface salinity contributing >80% of explanatory power. Spatial validation showed strong agreement with observed fishing activity (sensitivity: 0.98; specificity: 0.97), capturing seasonal patterns like monsoon-driven productivity and mesoscale eddies. While 85% of predictions fell within ±0.25 error thresholds, coastal discrepancies highlighted unresolved bathymetric and fishing pressure effects. The framework effectively tracked sub-mesoscale habitat dynamics across a 1,360 km domain. Key contributions include: (1)a transferable ML architecture for PFZ forecasting, (2) evidence-based prioritization of monitoring parameters, and (3) pathways for improvement via higher-resolution coastal data. This work advances tuna resource management and demonstrates the synergy of remote sensing and machine learning in marine spatial ecology.