Predictive modeling has gained great relevance in the agricultural sector, becoming a key tool for optimizing production and improving crop quality. This is because a well-designed model can reveal interactions that were not previously evident. By studying these relationships separately, simulated trials can be generated that, due to various limitations, could not be carried out in a real system. Predictive modeling in fruit crops is based on analyzing a wide range of data, such as images of plants, fruits, and leaves, genetic information, meteorological variables, biotic-abiotic factors, and economic factors. This article comprehensively reviews advances in predictive modeling in fruit production. The topics of interest are separated into four sections: Yield prediction using meteorological variables, prediction of pathogenic microorganisms, genetic improvement, and mobile apps designed with ML for the management and control of various fruit crops. This review aims to present the characteristics and importance of predictive modeling as a primary tool in decision-making processes.