Traditional optimization approaches for irrigation canal design have primarily focused on identifying cost-effective structural dimensions for simple cross-sections. However, these methods are inadequate for steep terrains where the construction of single or multiple rectangular inclined drops (RIDs) becomes essential. This study introduces a novel three-dimensional optimization model tailored for the optimal design of irrigation canals in such challenging environments. By integrating geospatial data with a particle swarm optimization (PSO) algorithm, the model establishes a continuous search space that facilitates the identification of cost-effective alignments while satisfying hydraulic and construction constraints. To validate its effectiveness, the model was applied to two synthetic case studies that feature varied terrain and slope conditions. Results demonstrated the model’s strong capability in optimizing both canal alignment and RID placement. Comparative analysis with genetic algorithm and ant colony optimization revealed that PSO outperformed both in terms of solution accuracy and consistency. Moreover, the proposed model produced results comparable to conventional design methods but with significantly reduced computational time. In addition, pre-cost-estimation tables were developed for various canal route alternatives and RID configurations, offering practical insights for efficient planning and preliminary design of irrigation canals in complex, sloping regions.