Accurate stock price prediction remains a pivotal challenge in the financial sector, essential for strategic decision-making by investors and financial institutions. Traditional statistical methods have long been used to forecast stock prices, but they struggle with the complexities and inherent unpredictability of financial markets. Machine learning (ML) models offer greater adaptability, and recent advancements in deep learning (DL) have further enhanced predictive capabilities by automatically extracting intricate data patterns. Among these DL techniques, graph neural networks (GNNs), particularly graph convolutional networks (GCNs), have shown exceptional promise by capturing spatial dependencies and temporal dynamics within financial data. This paper reviews the effectiveness of GCNs and introduces hybrid approaches that combine models like variational graph auto encoders (VGAEs), GCNs, LSTMs, and transformers to capitalize on both relational and sequential data structures. These hybrid models yield higher predictive accuracy, adaptability, and computational efficiency. Evaluating their performance with financial metrics like return-based evaluations underscores their practical applicability in stock market forecasting, positioning deep learning as a transformative tool in financial decision-making.