A method is presented to assist in the design of control and protection parameters for a Medium Voltage Direct Current (MVDC) system using a Single-Layer Perceptron (SLP) type of Neural Network. Unlike studies that typically verify control and protection parameters separately, in this work different operational scenarios are considered to analyze the joint performance of control and protection design. Then, a neural network is trained to learn the correlation between these parameters and the outcomes of success or failure cases. Tests are conducted using a modular multi-level converter (MMC) topology in an MMC-MVDC point-to-point system. The MMC-HVDC system is modelled in MATLAB/Simulink, and the SLP is developed using the Keras library (integrated with TensorFlow) in Python.