This study focuses on the calculation of Solar Radiation Pressure (SRP) and it's effects on the anticipated trajectory of a satellite in Low Earth Orbit (LEO). Using the primary Keplerian orbital characteristics, a simulated LEO satellite is analyzed to understand the influence of SRP.  The primary section of the document elucidates the methodology for computing the perturbation and assessing their impact on the projected trajectory, using a custom-developed Python module. Through this approach, the study highlights the variance in the satellite’s orbit over time, demonstrating how even minor forces can significantly alter the satellite’s position vector.

Additionally, this research explores the application of machine learning techniques to predict the behavioral patterns of orbital variations through the implementation of parametric predictive modeling. The incorporation of machine learning aids in developing more accurate and efficient methods for predicting changes in satellite trajectories, thereby improving the precision of orbital propagation.