The aim of this research is to design, develop, and evaluate a Multi-Agent Artificial Intelligence (MAS) framework that significantly increases the efficiency of banking services marketing processes by utilizing a distributed architecture. This study specifically focuses on operational data and digital transformation challenges at Rafidin Bank in Iraq. This study uses the applied-developmental research methodology and intelligent systems modeling. The proposed framework consists of four independent key agents: Data Analysis Agent (DAA), Offer Personalization Agent (PA), Campaign Execution Agent (CEA), and Coordinator Agent. The agents interact and collaborate with each other through algorithms such as behavioral clustering, reinforcement learning for offer optimization, and linear programming for budget allocation. The effectiveness of the framework was measured through simulation on Rafidin Bank’s historical data and by comparing financial and operational metrics (ROMI, conversion rate, and CAC) with traditional approaches. The results indicated the high effectiveness of the MAS framework in optimizing marketing processes. This framework was able to: Increase Return on Marketing Investment (ROMI) by 36.1%. Improve the Conversion Rate of target customers by 46.4%. Reduce Customer Acquisition Cost (CAC) by 29.1%, which is due to precise targeting and optimal channel management. These improvements are the result of the system's ability to make prescriptive decisions and execute operations in a distributed and real-time manner.