Precise modeling of dynamical systems can be crucial for engineering applications. Traditional analytical models often have shortcomings when capturing real-world complexities, particularly due to challenges in system nonlinearity representation and model parameter determination. Data-driven models, such as deep neural networks (DNNs), offer better accuracy and generalization but require large quantities of high-quality data. The present thesis introduces a novel method termed the Synthesized-Data Neural Network (SDNN), which integrates analytical models, which represent physics, with DNNs to enhance the dynamic model. The main steps of the present method are given below, with particular reference to the practical situation of a physical robot. The first three degrees of freedom (DOF) of a Kinova Gen3 Lite manipulator are formulated using the Euler-Lagrange equations of motion. The experimental data are recorded from the manipulator. Simulated data from the analytical model are combined with the experimental data to train the neural network. The model’s performance is evaluated using the Mean Squared Error (MSE) in real-time experiments with the Kinova Gen3 Lite manipulator. Training datasets represent 14 robotic trajectories, with the MSE calculated for four testing trajectories. The results obtained have led to the following conclusions:​ The SDNN model has shown improved performance in predicting joint torques when compared to the purely analytical model or the purely data-driven model. The SDNN, when trained with synthesized data from 14 trajectories (SDNN-14) achieved the lowest MSE of 2.1442, outperforming the analytical model (MSE of 2.8054) and the neural network trained solely on experimental data (MSE of 3.0521).