In this paper the Electric Eel Foraging Optimization (EEFO) algorithm was developed with inspiration from the way electric eels hunt for food. These eels perceive their environment and locate prey by producing electric fields. By imitating this natural behaviour, EEFO similarly looks for the optimal answer in an optimization problem. The algorithm cleverly strikes a compromise between exploitation (enhancing the best options discovered) and exploration (looking for new alternatives). This facilitates EEFO's more effective resolution of challenging issues. It works effectively and frequently outperforms conventional techniques in tests on various optimization tasks. EEFO's speed and precision make it a valuable tool for artificial intelligence, engineering, and other domains that require intelligent problem-solving