Artificial Neural Networks (ANNs) have gained significant attention in recent for their potential to model complex, non-linear relationships in various domains, including financial markets. In stock market prediction, ANNs are used to forecast stock prices, identify trends, and support investment decision-making. It explores the application of ANNs in stock market prediction, focusing on the underlying principles of neural networks, common architectures such as feed forward networks, recurrent neural networks (RNNs), and long short-term memory (LSTM) networks, which are particularly effective for time series forecasting. The ability of ANNs to capture intricate patterns and relationships in historical stock data, such as price movements, trading volume, and technical indicators, offers a promising avenue for predictive analysis. Despite challenges such as over fitting, the need for large datasets, and market noise, ANNs have demonstrated competitive performance when compared to traditional statistical methods. This study discusses various techniques to improve ANN model accuracy, including data pre-processing, feature selection, and model optimization. Ultimately, it highlights the growing roles of ANNs in financial forecasting and their potential to enhance market prediction strategies in the rapidly evolving field of algorithmic trading.