Disease and pest control models can generate information on agrochemical use only if necessary, reducing costs and environmental impacts. With machine learning algorithms, it is possible to develop models that will be used for diseases and pest warning systems to improve the effectiveness of chemical control over coffee tree pests. Therefore, infection rates are linked with climate change and measured and evaluated by machine learning algorithms for predicting the occurrence of diseases. Algorithms that are tested to predict incidence are (a)Multi-line regression (RLM); (b) K Neighbors Regressor (KNN); (c) Random Forest Regressor (RFT), and (d)Artificial Neural Networks. Pearson correlation analysis is to be considered under three different time periods,1-10 days (from 1-10 days before the incidence evaluation),11-20d, and 21-30d, and used to evaluate the unit correlations between the weather variables and infection rates. The number of days, maximum temperature, and relative humidity exceeding 80% are meteorological variables that show a significant correlation with this disease. There is a negative correlation with rainfall, and the severity of pests decreases with increasing rainfall. Machine learning algorithms can be used to predict diseases and pests.