The objective of this study is to develop a predictive maintenance algorithm for the ABB IRB 4600, a 6-axis robotic arm, using digital simulations. A variety of tests were conducted using SolidWorks, including calculations pertaining to stress, strain, fatigue, and heat. The simulations included an analysis of the materials used in the construction of the robotic arm, which are gray cast iron and aluminum alloy. The robotic arm was tested in three positions—picking, raised, and placing—with loads of 100 kg, 200 kg, and 300 kg, respectively. The findings indicated that elevated stress, strain, and displacement levels diminish the robot's operational lifespan and accelerate its deterioration over time. The placing position was found to experience the greatest stress, displacement, and strain. The fatigue test also demonstrated that after 10 million cycles, the arm had accumulated damage. The gradient boosting regression algorithm was selected as the Machine Learning (ML) algorithm for the study following a comparison of the performance of various ML regression models. This finding underscores the significance of predictive maintenance in preventing breakdowns and extending the robot's lifespan.