The growing complexity of financial markets has accelerated the adoption of artificial intelligence (AI) and big data analytics in wealth management. These technologies enable financial institutions to analyze vast datasets, optimize portfolios, and automate investment decisions. Cloud computing further enhances these capabilities by providing scalable, high-performance infrastructure for real-time financial decision-making. Despite these benefits, integrating AI with cloud computing presents challenges related to computational efficiency, data security, and scalability. Traditional financial models struggle to process real-time data effectively, leading to suboptimal investment strategies and increased risks. Ensuring secure, adaptive, and high-performance AI-driven decision-making remains a significant concern. This paper proposes a cloud-based AI-driven wealth management framework that utilizes machine learning, distributed computing, and homomorphic encryption to optimize portfolio allocation and enhance security. The multi-layered architecture integrates real-time financial data acquisition, AI-driven analytics, and scalable cloud deployment. Experimental results demonstrate that the proposed framework outperforms traditional models in investment return, risk mitigation, and computational efficiency. The findings highlight that cloud-optimized AI solutions reduce latency while ensuring security and regulatory compliance. These insights underscore the transformative potential of AI and cloud computing in modern wealth management, enabling secure, intelligent, and scalable financial decision-making systems.