Abstract: This research endeavour seeks to leverage the capabilities of a machine learning-based genetic algorithm for the optimisation process. The fields of machine learning and optimisation have experienced remarkable progress in their capacity to extract valuable insights from data. In conventional design optimisation, the simulation of a turbine configuration demands extensive computational resources. The novelty of this research lies in the application of a machine learning-based genetic algorithm for design optimisation, specifically within the realm of tidal turbines. This approach significantly accelerates the design of tidal arrays, aiding in the achievement of net-zero emissions by 2030.