This study proposes an ANFIS-based investment recommendation system for digital banking and e-government financial services, enhancing decision-making through artificial intelligence and machine learning. The system personalizes investment suggestions based on a customer’s financial situation, risk tolerance, and investment goals, aligning with the broader digital transformation of e-government and smart financial governance. Clustering techniques are utilized to categorize customers with similar financial behaviors, improving personalization and efficiency in automated financial advisory services. The system was tested using a dataset of 1542 potential investors, demonstrating its capability to classify customers accurately and offer tailored investment recommendations. By integrating AI-driven decision-making into e-government frameworks, this research highlights the potential for intelligent automation in public financial services. Future research directions include refining AI models for enhanced accuracy and aligning with digital governance policies. Overall, the proposed system represents a significant advancement in personalized digital banking, supporting smart governance and automated financial services in the era of digital transformation.