The importance of predicting crop yields lies in its significance for ensuring food security and optimizing agricultural practices. Precise crop yield forecasts empower farmers and policymakers to make well-informed decisions regarding harvesting, planting, and resource allocation, ultimately affecting the availability and affordability of food. While various methods for predicting crop yields exist, they often fall short in accuracy and efficiency. This research introduces the hybrid model as cuckoo search and particle swarm optimization (PSO + CS). This novel approach combines the optimization prowess of PSO + CS optimization with the data analysis capabilities of deep learning. This method involves several stages, including data setup and training, and employs a multi-layer architecture with specific layers dedicated to input, data preprocessing, classification, optimization, and output. This approach significantly improves the accuracy of crop yield predictions, addressing the limitations of existing methods. By harnessing the synergy of optimization and deep learning, PSO + CS empowers informed agricultural decision making, resource allocation, and food production efficiency, contributing to global food security.