<p>Influence maximization (IM) is an essential challenge in social network analysis, focusing on identifying a small group of highly influential nodes to amplify the flow of information, ideas, or product promotions. Given the NP-hardness of IM, standard approaches, such as greedy algorithms, while useful, become computationally unfeasible on large networks. Heuristic techniques based on centrality measurements are efficient, but they frequently lack accuracy in identifying the most influential nodes. This paper presents an Optimized Influence Maximization with a Budget Constrained Improved Capuchin Approach (OIM-BCI), which is based on an improved Capuchin Search Algorithm that integrates with Genetic Algorithm operators, crossover and mutation, to optimize seed node selection and increase influence spread within given budget restrictions. This approach targets applications similar to viral marketing, where maximizing advertising reach on a limited budget is critical. Experiments on three real-world datasets reveals its superiority in effectively identifying a cost-efficient seed set, exceeding established approaches regarding influence spread and computational efficiency. The OIM-BCI method demonstrates an improvement in influence spread under varying seed size, by a 3.23% gain over Capuchin Search Algorithm (CapSA), 14.65% over Multi-Objective Discrete Particle Swarm Optimization (MODPSO), and 39.86% over Multi-Objective Crow Search Algorithm (MOCSA). Under varying budgets, OIM-BCI shows a 3.34% gain over CapSA, 14.10% gain over MODPSO, and 37.69% gain over MOCSA. The results reveal that OIM-BCI is a scalable and cost-effective solution to the IM challenge, which is especially useful for marketing applications that demand optimal reach within financial restrictions.</p>