This paper presents an integrated approach for modelling and predicting gas turbine performance by combining thermodynamic modelling techniques and machine learning-based regression analysis. Traditional thermodynamic modelling, using the Brayton cycle and component performance maps, is a fundamental tool for analyzing gas turbine performance, but often faces limitations due to operational variabilities and nonlinearities. Machine learning techniques, such as linear regression, decision trees, and ensemble methods, are utilized to complement the thermodynamic model, providing more accurate predictions of key performance parameters like discharge pressure, temperature, and efficiency, particularly during off-design conditions. The proposed hybrid approach leverages thermodynamic models to simulate the physical processes of compression, combustion, and expansion, while machine learning refines these predictions using historical operational data. By integrating the physical laws governing gas turbines with adaptive machine learning algorithms, this paper demonstrates improved prediction accuracy, robustness in transient operations, and enhanced efficiency in gas turbine monitoring and predictive maintenance. The results indicate a 15% reduction in prediction error when compared to using thermodynamic or machine learning models alone. This integration aims to bridge the gap between physics-based modeling and data-driven techniques, offering a comprehensive solution for optimizing gas turbine operations. The hybrid approach not only ensures real-time adaptability but also facilitates predictive maintenance, minimizing operational risks and extending component life cycles