This paper presents a risk-aware intelligent energy management strategy tailored for renewable-integrated hybrid energy systems. The proposed framework combines battery storage, compressed-air energy storage, an electric-vehicle parking facility, and multiple renewable generation units within a smart distribution network. The renewable portfolio includes wind turbines, photovoltaic panels, and small-scale hydro turbines. To enhance operational performance, the strategy employs an optimization model that maximizes the expected profit of hybrid energy systems, supported by a market-participation risk assessment framework. The optimization problem incorporates optimal power-flow constraints, comprehensive risk-evaluation models, and detailed operational formulations for both storage and generation resources. The methodology explicitly considers uncertainties in electricity market prices, renewable power generation, load demand variations, and electric-vehicle behavior. These uncertainties are addressed through a stochastic optimization approach based on the Point Estimation Method. The sustainable computation-based Artificial Intelligence (AI) optimization algorithm including the Gray Langurs Optimizer is used to solve the resulting optimization problem. Extensive numerical studies confirm the robustness and effectiveness of the proposed strategy, demonstrating significant improvements in the economic performance of hybrid energy systems as well as enhanced operational efficiency of the distribution network. Network utilization improves by 27.8% to 86.8% compared with conventional load-flow analysis techniques. The solution algorithm achieves an accurate optimal solution with maximum profitability and a notably low response standard deviation of 0.91%, all within a short computational time.