This paper presents a novel application of Computational Intelligence to optimize dietary plans tailored to athletes’ unique nutritional needs across diverse sports disciplines. Leveraging Evolutionary Algorithms (EAs), the system dynamically generates personalized meal plans based on multi-factorial inputs, including age, gender, sport type, and individual food preferences. By integrating multi-objective optimization, the approach ensures precise macronutrient balance for enhanced performance, recovery, and long-term health. The system draws from a structured food database, offering real-time adaptability to dietary restrictions and preferences, and delivering nutrient-dense meal plans optimized for metabolic efficiency. Unlike traditional methods, this solution provides a practical, cost-effective alternative to professional diet planning. The research underscores the transformative potential of EAs in sports nutrition, combining tailored recommendations with scientific accuracy to set a new standard for athlete-focused dietary optimization.