Access to reliable, economical, and sustainable energy is a critical challenge in remote communities where infrastructure constraints and unreliability of renewable energy sources (RESs) complicate the possibility of having a stable supply. This study is motivated by the urgent need for intelligent, adaptive energy management systems that can ensure the reliability of the supply while maximizing the use of RESs. To meet this need, an adaptive and scalable multi-agent system (MAS) framework for hybrid energy systems can be employed. The system includes electric vehicle batteries (EVBs), hydrogen energy storage systems (HESSs), and battery energy storage systems (BESSs) and wind turbines (WTs) and PV. A hybrid backup architecture for energy supply continuity in low availability of RESs, in addition to vehicle-to-grid (V2G) functionality enabling EVBs to support grid stability. The MAS is evaluated under four scenarios: PV–WTs–BESSs, PV–WTs–BESSs–EVBs, PV–WTs–BESSs– HESSs, and PV–WTs–BESSs–EVBs–HESSs. Scenario 4 attains the lowest operating cost of $10,688.06, a reduction of 0.91% from scenario 1, in a 25 kW peak load microgrid. The artificial gorilla troops optimizer optimizes the real-time energy dispatch by learning to adjust to changing system conditions. Simulation results confirm that the proposed MAS improves cost-effectiveness, energy stability, and sustainability in constrained settings.