The transition towards intelligent power networks is accelerating with the rise of Society 5.0, demanding decentralized, data-driven, and resilient energy systems. However, existing solar energy optimization approaches often lack adaptive automation, governance integration, and predictive control—leading to inefficiencies and unbalanced grid loads. This paper proposes an IoT-enabled smart governance framework that leverages edge computing, artificial intelligence, and a novel Dynamic Solar Tracking Optimization (D-STO) algorithm for real-time solar panel adjustment and energy distribution. The architecture integrates environmental sensing, AI-based prediction, and cloud-supported decision governance to optimize solar output, minimize downtime, and enhance grid coordination. Field deployment on a 335W panel system demonstrated a 20% increase in energy yield, 15% reduction in equipment downtime, and 25% drop in maintenance interventions compared to static and traditional tracking systems. Additionally, the system achieved latency reductions of over 80% through edge processing while aligning with sustainability targets. This work contributes a scalable, policy-aligned model for intelligent energy automation, bridging the gap between smart energy systems and Society 5.0 goals.