Artificial Intelligence (AI) and machine learning (ML) have revolutionized the realm of stock market prediction, offering sophisticated tools to analyze vast volumes of data and anticipate market trends. This article provides a comprehensive overview of AI techniques, focusing on Python as the preferred platform for implementation. Beginning with an exploration of AI fundamentals, including machine learning and deep learning, it delves into various techniques employed for stock market prediction. Traditional statistical models such as linear regression and ARIMA are under scientific discussion alongside advanced ML algorithms like random forests and support vector machines. Moreover, the article highlights the efficacy of deep learning methodologies, particularly recurrent neural networks (RNNs) and long & short-term memory (LSTM) networks, in capturing temporal dependencies within stock market data. We also explored innovative developments such as Generative Adversarial Networks (GANs) for their potential in revealing hidden patterns influencing price movements. Throughout the discussion, we concluded that Python emerges as the preferred programming language due to its simplicity, extensive libraries, and versatility. Key libraries such as Pandas, NumPy, Scikit-learn, and TensorFlow play a pivotal role in data manipulation, preprocessing, and model development. The article outlines a structured approach to building predictive models, encompassing data collection, preprocessing, feature engineering, model selection, training, evaluation, and prediction. Despite the advancements in AI, challenges persist in stock market prediction, including market volatility, data quality issues, complexity of influencing factors, and risks of overfitting. Ultimately, we may witness AI and Python synergy, which empowers analysts and investors with deeper insights, enabling informed decision-making amidst the complexities of financial markets.