The increasing complexity and volatility of financial markets have highlighted the limitations of traditional methods of technical analysis to give an accurate prediction of stock price movements. This study aims to address this challenge by exploring the role of artificial intelligence (AI), particularly machine learning, deep learning, and hybrid models, in refining technical analysis for stock price predictions. The research reviews the application of AI techniques, evaluates their advantages over conventional methods, and examines their integration with key themes such as forecasting, sentiment analysis, and technical indicators. Findings reveal that AI-driven models significantly outperform traditional approaches by capturing non-linear patterns, temporal dependencies, and real-time market dynamics. Machine learning algorithms excel in structured data analysis, while deep learning architectures like Long Short-Term Memory (LSTM) networks demonstrate superior performance in handling sequential data. Hybrid models further enhance the accuracy by combining technical, fundamental, and sentiment analysis, offering a comprehensive view of market behavior. The study underscores the theoretical contributions of AI in advancing financial forecasting methodologies and its practical implications for investors, analysts, and policymakers. By enabling data-driven decision-making, AI models can improve market efficiency, mitigate risks, and maximize returns. However, challenges such as data quality, model interpretability, and adaptability to unprecedented events remain. The study concludes by suggesting future research directions, including the development of explainable AI frameworks and the integration of alternative data sources. These advancements hold significant potential for transforming financial forecasting and driving innovation in investment strategies, ultimately enhancing the resilience and adaptability of global financial markets.