This study investigates the transformative potential of artificial intelligence (AI) in portfolio management across diverse financial markets. It aims to develop a comprehensive framework that enhances investment strategies in developed, emerging, and frontier markets through AI techniques. The research will employ a mixed-methods approach, combining quantitative and qualitative methodologies. Initially, extensive secondary data will be collected from market reports, financial databases, and previous studies. This data will encompass market-specific characteristics, AI-based portfolio strategies, and financial indicators. AI methods, including neural networks, machine learning models, and natural language processing applications, will then be utilized to analyze portfolio performance across various market scenarios. The anticipated outcome of this study is a versatile AI-driven framework that accommodates the unique aspects of different financial markets. By optimizing asset allocation, risk management, and predictive analytics, this framework will provide portfolio managers with evidence-based recommendations to enhance the stability and performance of their investments. The study aims to demonstrate how AI can mitigate risks associated with market volatility, liquidity issues, and regulatory constraints, leading to a more resilient and data-driven approach to portfolio management on a global scale