Accurately forecasting financial time-series data is a challenging task due to the dynamic and volatile nature of stock markets. This study introduces a hybrid Temporal Convolutional Network (TCN) and Long Short-Term Memory (LSTM) model designed to improve stock price forecasting for the Muscat Securities Market (MSM). Unlike standalone deep learning models, this hybrid approach effectively captures both short-term and long-term dependencies, leading to improved predictive accuracy. Trained on 24 years of historical MSM data (2000–2024), the model was evaluated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The hybrid model outperformed both standalone architectures, achieving the lowest MAE (206.29) and RMSE (314.31). This research advances financial forecasting by introducing a hybrid TCN-LSTM model specifically optimized for the Muscat Securities Market (MSM), a relatively underexplored financial domain. The study bridges the gap in existing models by enhancing predictive performance through an innovative fusion of deep learning techniques. The study contributes to financial forecasting research by demonstrating how hybrid deep learning models can enhance market prediction accuracy, providing valuable insights for investors and financial analysts. Future research directions include the integration of adaptive learning mechanisms and external financial indicators for further performance enhancement.