A healthy individual is one who is in good emotional, psychological, and social well-being. It affects how an individual think, feel, and behave each day. But due to rapid pace in digitization, and other factors had contributed to a serious cognitive decline, observed across both young adults and elderly which may potentially manifest into various neurological disorders. This research paper explores the utilization of metaheuristic algorithms to generate therapeutic sound frequencies aimed at alleviating mental health issues. Metaheuristics, including artificial bee colony (ABC), ant colony optimization (ACO), and Tabu search, are employed as robust problem-solving strategies to navigate the complex solution space efficiently. These nature-inspired techniques are analyzed for their efficacy in optimizing sound frequencies to achieve the best fitness values, tailored to therapeutic needs. The study provides a comparative analysis of these algorithms, highlighting their respective strengths, weaknesses, and the impact of various hyper-parameters on their performance. Additionally, the research delves into the challenges encountered in the application of these techniques and suggests avenues for future exploration. By integrating advanced metaheuristic approaches, this work represents a significant step toward innovative mental health interventions, offering a promising methodology for addressing neurological disorders through optimized therapeutic sound frequencies.