This paper presents a dual-mode artificial intelligence-based control approach for robust regulation of DC-DC two-switch forward converters under wide input voltage fluctuations and dynamic load conditions. Two neural network-driven strategies are explored: (1) an adaptive PI controller whose gains are optimized offline via a genetic algorithm and predicted using a feedforward neural network, and (2) an online neural network controller that directly computes the duty cycle in real time using online learning. Extensive simulation studies compare these neural approaches against classical PI and adaptive fuzzy controllers using various quantitative performance metrics, including steady-state error, overshoot, peak time, settling time, and integral error indices (IAE, ISE, ITAE). Simulation results under nominal conditions and multiple disturbance scenarios—including sudden load changes and input voltage drops—demonstrate that both AI-based approaches outperform conventional PI and fuzzy controllers. While the offline-trained adaptive PI controller excels in nominal conditions with minimal steady-state error and overshoot, the online neural network controller offers superior dynamic adaptability and robustness in real-time scenarios. Comparative performance metrics and radar-based multi-criteria evaluations confirm the suitability of these intelligent control strategies for real-time power electronics applications requiring high precision and resilience