The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) has transformed modern financial systems from predictive analytics to autonomous decision-making. This paper presents an Intelligent Financial System (IFS) architecture that integrates predictive modeling with an Autonomous Decision Model (ADM) driven by reinforcement learning and optimization. The proposed framework leverages big data technologies and hybrid ensemble learning to process high-velocity financial data efficiently, while the ADM enables self-learning, adaptive financial decision-making in dynamic markets. Experiments using multi-source financial datasets validate the model’s ability to enhance prediction accuracy, decision stability, and risk-adjusted returns. The study also highlights the challenges of ethical AI, data transparency, and regulatory compliance in financial automation. The proposed approach contributes to the advancement of data-driven, interpretable, and autonomous systems that improve decision precision and trust in digital finance.