This research aims to address various unresolved technical challenges in industrial robotics and soft robotics using artificial intelligence and digital engineering tools. The first part of this study investigates the impact of cracks on the vibration characteristics of rigid robot links as cracks can degrade the performance and reliability of robotic systems. Using the finite element method (FEM), simulations were conducted on planar robot link models with and without artificial cracks of varying sizes, locations, and orientations. Their vibration responses were measured and then analyzed by using machine learning along with the Gramian Angular Summation Field (GASF) method that converts the vibration data into 2D images for crack detection. The results demonstrated 98.25% accuracy in crack detection, showcasing the feasibility of the proposed approach. The second part of this study explores the emerging field of soft robotics, which has garnered significant attention due to its potential to enhance flexibility, safety, and productivity in manufacturing. Soft robots, which are constructed from compliant materials such as silicones, exhibit superior adaptability in unstructured environments and facilitate safer human–robot interactions. Despite these advantages, challenges still persist in achieving precise motion control and stiffness compliance. This research investigates the motion of pneumatic soft robots under varying loading conditions using finite element analysis (FEA) and machine learning techniques. A novel asymmetric double-chamber soft robot design was proposed by demonstrating its ability to achieve a considerably larger reachable workspace compared to conventional single-chamber soft actuator designs. A machine learning model was established and then trained with simulation data to accurately predict the kinematics and workspace of the soft robot. It was able to demonstrate predictions with an R-squared value of 0.99 and a root mean square error (RMSE) of 0.783, providing valuable insights into optimizing such soft robot performances. In addition, comparative analysis of the workspaces of asymmetric double-chamber and single-chamber soft robots revealed that the double-chamber design offers approximately 185 times more reachable workspace than the single-chamber configuration.