The advancement of solar-powered IoT nodes necessitates robust energy management strategies to enhance their operational efficiency. Optimizing energy consumption remains a key challenge with the increasing deployment of optical fiber sensors in environmental and industrial monitoring. This paper presents an adaptive energy management framework integrating artificial intelligence (AI) and fuzzy logic techniques to optimize energy utilization in solar-powered IoT nodes. The proposed approach ensures sustained operation under varying environmental conditions by dynamically adjusting power distribution and load prioritization. Simulations were conducted using reinforcement learning and fuzzy logic to analyze energy efficiency. The AI model exhibited superior energy utilization, reduced wastage, and enhanced reliability compared to conventional fuzzy-based control systems. The study provides insights into the role of intelligent power allocation in improving optical fiber sensor performance.