Heavy metal contamination, particularly from lead (Pb), represents a critical environmental and public health concern owing to its persistence, high toxicity, and pronounced tendency for bioaccumulation in ecological and human systems. In this context, the present study investigates the effectiveness of electrocoagulation (EC) as a treatment technology for the removal of Pb²⁺ from aqueous solutions, coupled with a systematic process optimization approach. A Box-Behnken experimental design, integrated within the framework of response surface methodology (RSM), was employed to evaluate the individual and interactive effects of key operational parameters, namely pH, current density, and reaction time, on Pb²⁺ removal efficiency. The results clearly indicate that pH is the most influential variable, with optimal removal performance observed under near-neutral conditions (pH 7-8), where the formation of metal hydroxide flocs is maximized. In comparison, current density and reaction time exhibited moderate yet complementary influences, primarily through their roles in enhancing coagulant generation and facilitating floc growth and aggregation. The developed quadratic model demonstrated high statistical significance and excellent predictive capability, as reflected by a coefficient of determination (R²) of 0.993. The strong agreement between experimental observations and model predictions confirms the robustness and reliability of the optimization framework. Under the identified optimal conditions-pH 7.6, current density 55 A/m², and reaction time 38.5 minutes-the experimental Pb²⁺ removal efficiency reached 98.35%, closely aligning with the model-predicted value of 99.82%. The minimal deviation between these values further validates the accuracy of the model and its suitability for predictive and optimization purposes. The findings of this study demonstrate that electrocoagulation is an efficient, cost-effective, and environmentally sustainable method for the removal of Pb²⁺ from contaminated water. Moreover, the integration of EC with advanced statistical optimization techniques such as RSM provides a powerful and reliable framework for enhancing process performance. These results support the potential application of EC technology in real wastewater treatment systems and contribute to the development of optimized strategies for heavy metal remediation.