The fourth most consumed food in the world, potatoes are a crop of great importance for food security. To fully explore their potential, it is essential to expand productivity studies, enabling yield to be predicted more efficiently. This study aimed to integrate remote sensing data and machine learning algorithms to develop yield prediction models for potatoes across two cultivars. For this purpose, yield samples were collected in a commercial field cultivated with two cultivars: Asterix and Markies. The sampling grid consisted of 160 points, with 80 points assigned to each cultivar. For sampling, a frame was used within which all tubers were collected and weighed. Satellite imagery from the PlanetScope platform, captured 25 days before crop desiccation, was used to calculate vegetation indices: NDVI, SAVI, and GNDVI. To develop the models, machine learning algorithms were employed: K-Nearest Neighbors, Support Vector Regression, Ridge Regression, Random Forest, XGBoost, Gradient Boosting, and Decision Tree — all of which are supervised learning methods. Given the differences between cultivars, we opted to generate separate models for each, as well as a combined model using both cultivars, in order to assess which dataset would yield more accurate predictions. The dataset was split into 80% for model training and 20% for testing. The Random Forest algorithm stood out by producing the most accurate models when using the combined cultivar dataset, with a mean absolute error (MAE) of 4.58 t·ha⁻¹. These results highlight the potential of integrating remote sensing (RS) and machine learning algorithms — particularly Random Forest — for predicting potato yield. Further studies aimed at expanding the dataset will be essential for obtaining more precise and reliable models.