Robotic surgery uses robots to help doctors perform operations it gives them better accuracy, control, and flexibility than regular surgery methods so important because it lets doctors do less invasive procedures which means shorter recovery times, fewer problems, and better results. This is a big improvement in modern healthcare. In this research, the robotic surgery outcome is predicted by a machine learning model. It is a part of artificial intelligence that allows computers to learn from information and make choices on their own, without needing detailed instructions. The study investigates the application of sophisticated computing methods to anticipate the outcomes of surgeries performed by robots using Support Vector Machines (SVM) along with ensemble techniques to make predictions more accurate. This helps keep patients safe and assists surgeons in making better decisions based on data for improved results. The aim of the study to predict the Robotic Surgery Outcome is to improve the ability to predict how well surgeries will go by using SVM along with the stacking technique. This method makes things more accurate to help predict problems and assists surgeons in making smart choices based on data which leads to better safety for patients and more successful surgeries. The future of using machine learning in predicting the results of robotic surgery includes making models better over time providing support for decisions as they happen and creating personalized treatments for patients after gathering more data to predict models will improve results, lower risks and make surgeries more accurate because it will lead to better, easier and more available healthcare for patients everywhere.