Agricultural productivity is influenced by a complex interplay of environmental conditions, soil characteristics, and farm management practices. Traditional farming methods often lack the precision and adaptability required to optimize these dynamic variables, limiting crop yield potential and sustainability. In response to this challenge, this study presents an intelligent crop optimization framework that leverages the capabilities of bio-inspired metaheuristic algorithms, Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Inspired by natural phenomena such as bird flocking and ant foraging, PSO and ACO are employed to explore optimal combinations of key agricultural parameters, including soil nutrient composition, pH levels, rainfall, and temperature. A neural network is trained to evaluate these parameter configurations, providing a performance-guided feedback mechanism that directs the search toward high-yield solutions. By integrating PSO and ACO with a neural predictive model, the proposed hybrid system combines the global search power of evolutionary algorithms with the pattern recognition strength of deep learning. This synergy enhances both the accuracy and robustness of decision-making in agricultural settings. The model not only adapts to changing environmental inputs but also supports real-time optimization, making it highly suitable for precision agriculture applications. Experimental results demonstrate that the system can effectively recommend parameter configurations that maximize yield while maintaining resource efficiency. The proposed approach offers a scalable, data-driven solution that empowers farmers with intelligent tools for informed and sustainable agricultural planning. This study contributes a novel and adaptive computational framework for optimizing crop yields, bridging the gap between artificial intelligence and modern farming practices.