Energy load forecasting is a systematic method of predicting future loads in advance to maintain a balance between energy demand and supply. Sustainable energy development is critical in energy design, planning, generation, transmission, and distribution, which leads to computational complexities in conventional methods when it is non-linear. Bio-inspired optimization algorithms are part of artificial intelligence that is inspired by the biological behavior and evolutionary principles of nature. Due to advancements in artificial intelligence, an increasing number of heuristic optimization techniques have resulted in the efficient handling of complex problems. The complexities in the accurate prediction of electricity can be achieved with evolutionary algorithms and swarm intelligence optimization algorithms. It avoids unnecessary expenditure, overestimation, and underestimation of electric energy and maximizes capacity utilization. This paper reviews recent studies on population-based optimization techniques to handle complexities in energy load forecasting and smart energy management. [ABSTRACT FROM AUTHOR]