Abstract
Current energy management faces its most significant power optimization struggle because rising demand meets with environmental pressures and requires effective resource utilization. The research develops an advanced implementation of bitterling fish optimization (BFO) combined with the genetic algorithm (GA) to perform power consumption optimization in intricate systems. The proposed framework connects BFO swarm intelligence to GA evolutionary strategies through machine learning adaptation, which simultaneously minimizes power losses and improves system efficiency throughout multiple applications. The hybrid approach produces quicker convergence through improved optimization performance and delivers flexibility toward dynamic energy requirements. The model requires consideration of three vital elements: load distribution management and renewable integration, and power loss reduction, along with interactive visual controls to monitor power distribution and optimization progress, and system output. Additionally, cloud and edge computing technologies enable large-scale data processing and ensure low-latency optimization for real-time applications. The framework also supports adaptive learning mechanisms, allowing it to continuously refine optimization strategies based on real-time energy data and consumption patterns. The proposed power optimization framework has wide-ranging applications in smart grids, industrial automation, and renewable energy systems, offering a scalable, high-accuracy solution for modern energy management. The integration of predictive analytics and real-time monitoring empowers decision-makers with proactive energy-saving strategies, ultimately contributing to a sustainable, efficient, and intelligent power infrastructure. This research provides a novel, data-driven approach to power optimization, addressing critical challenges in energy management while enhancing reliability and operational efficiency in future energy infrastructures.