Modern smart grid systems face significant challenges in minimizing transmission losses, maintaining voltage stability, and efficiently integrating renewable energy resources under dynamic operating conditions. Existing optimization approaches mainly focus on single-objective control strategies and lack adaptive preventive learning capability for real-time smart grid management. To address these limitations, this research proposes a Preventive Synaptic Learning based Smart Grid Optimization (PSLSGO) framework for intelligent energy management and power distribution optimization. The proposed framework integrates adaptive synaptic learning, preventive fault prediction, intelligent power routing, reactive power compensation, adaptive load balancing, and renewable energy coordination within a unified optimization architecture. Real-time electrical parameters including voltage, current, frequency, load demand, transformer temperature, and renewable energy generation were collected using IoT-enabled monitoring systems and evaluated using the IEEE 33-bus smart distribution network. Experimental results demonstrated that the proposed PSLSGO framework achieved 98.4% energy efficiency, 98.1% reliability, 48.9% transmission loss reduction, 97.2% voltage stability, and 92.5% renewable energy utilization. Statistical validation using ANOVA and confidence interval analysis confirmed the significance and reliability of the obtained results. The adaptive synaptic learning mechanism significantly improved prediction accuracy and preventive control capability under fluctuating operating conditions. Overall, the proposed PSLSGO framework provides an intelligent, scalable, and energy-efficient solution for next-generation smart grid optimization and sustainable power distribution systems.