The pathogen Fusarium oxysporum is the major limiting factor in the production of the cape gooseberry crop Physalis peruviana, due to the inexperience of growers to identify and manage it. The objective of this research was to identify the relationship between the chemical characteristics of the soil and the presence and absence of Fusarium oxysporum. Soil samples were collected from 100 cape gooseberry production units. This process was carried out in plots with healthy plants and plants affected by the pathogen Fusarium oxysporum Schltdl. Descriptive analysis, Pearson correlations, principal components (PCA) and Machine Learning models were used to analyze the information associated with the chemical elements of the soil of cape gooseberry farms. The Decision Tree Classifier model showed the best predictive performance with Accuracy metrics of 0.58, Recall of 0.57, and F1 Score of 0.51...