As electronic devices become more compact and powerful, effective thermal management is crucial to prevent overheating. Honeycomb heat sinks have emerged as a promising solution due to their unique geometry, which maximizes surface area while minimizing weight. This chapter presents the multi-objective optimization of a honeycomb heat sink using the Sunflower Optimization Algorithm (SFO), focusing on maximizing the Nusselt number and minimizing the friction factor. The optimization is performed by adjusting five design parameters: fin height, fin thickness, longitudinal pitch, angle of attack, and Reynolds number. Quadratic equations are derived to model the relationship between these parameters and the heat sink’s performance. The results from the SFO algorithm are validated using the augmented ε-constraint method, and the generated Pareto-optimal solutions provide insights into the trade-offs between heat transfer efficiency and pressure drop. The findings highlight the efficiency of the SFO algorithm in optimizing complex, multi-objective problems in thermal management systems.