The use of Artificial Intelligence (AI) to solve different types of problems has spread to other areas of knowledge, such as business, marketing, health sciences, and architecture, to name a few. In Engineering, specifically product design, where CAD/CAM/CAE software applications are used to assist the user in the design, manufacturing, and engineering analysis, AI tools have also been implemented to obtain better generative product designs; however, a combination of methodologies has not been used to improve these processes. This work explores the Multi-Strategy Learning (MSL) environment to design products through AI tools combining different learning model approaches; the above is the novelty of this work. Initially, unsupervised learning is used to extract common patterns from a set of products of the same type to be designed. Later, a supervised learning model predicts how good a design is. Finally, a random search algorithm with Greedy evaluation evaluates the designs generated by the previous models and delivers the best design according to the earlier results. The results showed that using supervised learning models requires a more significant amount of data to obtain better predictions, which contrasts with unsupervised learning models. It was also determined that applying the MSL environment allows an effective alternative for generating new product design proposals, allowing the user to select the most appropriate one.