Belg crop production refers to the agricultural yield and performance of crops grown during the Belg season, which is the short rainy season in North Wollo, Ethiopia; typically occurring from February to May. Accordingly the study area was purposefully selected from the 11 zones of the Amhara region due to its significant challenges related to food insecurity and its substantial capacity for cropping during both the Meher and Belg seasons. Therefore, this study aims to closely evaluate Belg crop production using metrological information’s, mois ture availability information’s, and crops (vegetation’s) availability information’s. To evalu ate these, this study employs multi-source data derived indices and explanatory variable that were integrated with non-parametric machine learning techniques. To validate the satellite (indices) information’s, a purposive sampling method was used to collect crop production data from all 104 kebeles identified as having Belg crop potential and related information’s in the study area. This integrated information enabled the validation of satellite data and fa cilitated a comprehensive analysis by using mixed method of explanatory sequential research design. Finally, machine learning algorithms was analysed in this study (Random Forest Re gression, ANN, and SVM), among those the Random Forest algorithm was well performed in predicting Belg crop production, achieving an R² value of 0.81 and a mean absolute error (MAE) of 699.19. In random forest machine learning algorithm, all variables derived from remote sensing satellites play a significant role in indicating Belg crop produc tion; with NDVI, NDRE, SAVI, and LST being the most effective indices in predicting Belg crop production in this study. Generally, this study offers key insights into Belg crop production potential regions in the study area, and illustrating how advanced remote sensing and machine learning algorithms helps to understand agricultural production potential areas in North Wollo, Amhara region.