This study introduces a novel hybrid methodology that integrates Particle Swarm Optimization (PSO) and Social Network Analysis (SNA) to enhance conversion prediction in Meta digital advertising campaigns. Real-world data was collected from four international Meta ad accounts promoting coaching programs, structured across three dimensions: demographic targeting (age, gender), platform placement, and daily performance metrics. Following an initial cleaning and balancing process, a baseline Random Forest classifier was trained, achieving an F1-score of 0.81. PSO was then employed to optimize hyperparameters, resulting in an improved F1-score of 0.83. In parallel, we applied SNA techniques to construct behavioral graphs based on feature similarity and centrality, generating new network-based predictors. Finally, we combined both optimized hyperparameters and SNA-derived features into a hybrid PSO-SNA model. The hybrid model demonstrated superior performance, achieving an accuracy of 0.89, precision of 0.90, recall of 0.90, and an F1-score of 0.87, significantly outperforming previous models. These results underscore the effectiveness of integrating topological behavioral insights with traditional optimization techniques. By bridging swarm intelligence and social graph analysis, this hybrid model offers a scalable and explainable approach for advertisers seeking to maximize conversion rates without altering Meta’s black-box algorithms.