The field of soft robotics has been experiencing rapid growth, with researchers and engineers showing increasing interest due to the unique capabilities of these robots. Soft robots, characterized by their soft bodies and flexible structures, have demonstrated great potential in addressing real-world challenges across various domains, including medical applications. Effective modeling and control are vital for fully harnessing the potential of soft robots, particularly in applications involving human interaction. However, creating models for soft robots made of soft materials, diverse shapes, and actuators poses significant challenges. Moreover, accurate fault detection in soft robots necessitates precise modeling. This paper introduces a novel machine learning approach, termed deterministic learning, for training a soft robot model using a radial basis function neural network. The research explores the fault detection process by simulating four distinct faults that could impair system control performance, such as diminishing tracking accuracy or inducing instability. Furthermore, the paper examines the identification of fault occurrences during the operation of soft robots.