The developed Active Roll Stabilization (ARS) system for naval vessels integrates Model Predictive Control (MPC), Neural Networks (NN), H ∞ robust control, and fuzzy logic to enhance stability, robustness, and energy efficiency. This approach is optimized for the dynamic operational requirements and unique designs of naval ships, unlike traditional ARS systems. MPC allows real-time adjustment using current and forecasted data, which brings proactive stabilization in rapidly changing sea conditions. NN adjusts the system to specific operational parameters and structural attributes of naval vessels to ensure high performance across diverse mission profiles. H ∞ robust control enhances the system’s resistance against adverse sea conditions, extreme sea states, etc., whereas fuzzy logic achieves a compromise between stability and energy efficiency during extended long naval missions. The system uses predictive modeling that merges historical data with advanced simulations in order to give accurate roll stabilization tailored to naval operations. Simulations show an improvement in the reduction of roll and robustness when exposed to more severe maritime conditions, as well as energy-saving capabilities. The empirical validations demonstrate the effectiveness of the system within advanced naval platforms like destroyers, frigates, and aircraft carriers.