Agroecological systems are a potential solution to the environmental challenges of intensive agriculture. Indigenous communities, such as the Kamëntšá Biyá and Kamëntšá Inga from the Sibundoy Valley (SV) in Colombia have their own ancient agroecological systems called chagras. However, they are threatened due to population growth and the expansion of intensive agriculture. Establishing new chagras or enhancing existing ones faces impediments, such as the necessity for continuous monitoring and mapping of agroecological potential. However, this is often costly and time-consuming. To address this limitation, we created a digital map of the Biodiversity Management Coefficient (BMC) (as proxy of agroecological potential) using Machine Learning. We utilized 15 environmental predictors and in-situ BMC data from 800 chagras to train a XGBoost model capable of predicting a multiclass BMC structure with 70% Accuracy. This model was deployed across the study area to map the extent and spatial distribution of BMC classes, providing detailed information on potential areas for new agroecological chagras as well as areas unsuitable for this purpose. This map captured the footprints of past and present disturbance events in the SV, revealing its usefulness for agroecological planning. We highlight the most significant predictors and their optimal values triggering higher BMC status.