This thesis focuses on the use of machine learning for the design, optimization, and performance analysis of rotors. The ML models were trained using a public dataset provided by the University of Illinois, containing both data related to rotor geometry and performance data under static conditions, in which the advance ratio J is zero, and dynamic conditions, in which J > 0. The training was carried out using MATLAB’s Regression Learner, selecting the most suitable models based on prediction errors. During the course of the work, two codes were developed, designed to be flexible and scalable. The first allows for the design of rotors starting from performance or geometric specifications provided as input; the user can choose whether to generate a Pareto front using a multi-objective genetic algorithm, or to directly obtain a single configuration through a standard genetic algorithm.