In this research, a Temporal Convolutional Network (TCN) is combined with a Transformer model with multi-head attention to present a novel approach to stock price forecasting. The primary objective is to address the challenges of recognizing complex patterns and long-term interdependence inherent in the volatility of financial time series data. By fusing the powerful attention mechanisms of Transformers with the sequential processing capabilities of TCNs, the hybrid model provides a powerful solution. This method performs better than conventional deep learning models, including Long Short-Term Memories and standalone TCNs, according to extensive testing on historical stock market data. The outcomes highlight the efficacy of this approach for trustworthy stock market forecasting by demonstrating notable gains in prediction accuracy and model stability.