This paper presents an integrative dynamic model of Colombian population distribution based on the maximum entropy principle and the flows of matter, energy, and information. The model integrates the balances of related variables as macro-state restrictions, using electrical consumption, water demand, and higher education rates as key indicators. The human population is treated as a convergence variable of these three physical entities, and the model predicts population distribution yearly by combining these variables. The model includes statistical moments for previous population distributions, allowing for the exploration of future dynamics. The implications of this model can contribute to bridging information sciences and sustainability studies. The model is based on the concept of social-ecological systems (SES), where information, matter, and energy flows are considered as active variables. The paper discusses the importance of information in SES, highlighting its role as a key currency for energy and matter transformations. The model uses the maximum entropy principle to integrate variables related to information, matter, and energy, and to predict population distribution in Colombia. The model's results show that the inclusion of statistical moments improves the accuracy of population distribution predictions. The model was tested using data from the 1985, 1993, and 2005 censuses and projections to 2020. The results show that the model performs well in predicting population distribution, with relative errors below 10% for the first ten statistical moments. The model also shows that the inclusion of statistical moments improves the accuracy of population distribution predictions, especially for low-populated regions. The model's results suggest that information can be considered as an active variable in SES, and that the integration of information, matter, and energy flows is essential for understanding population dynamics in social-ecological systems. The model has potential applications in regional and national management, including resource management and regional planning.This paper presents an integrative dynamic model of Colombian population distribution based on the maximum entropy principle and the flows of matter, energy, and information. The model integrates the balances of related variables as macro-state restrictions, using electrical consumption, water demand, and higher education rates as key indicators. The human population is treated as a convergence variable of these three physical entities, and the model predicts population distribution yearly by combining these variables. The model includes statistical moments for previous population distributions, allowing for the exploration of future dynamics. The implications of this model can contribute to bridging information sciences and sustainability studies. The model is based on the concept of social-ecological systems (SES), where information, matter, and energy flows are considered as active variables. The paper discusses the importance of information in SES, highlighting its role as a key currency for energy and matter transformations. The model uses the maximum entropy principle to integrate variables related to information, matter, and energy, and to predict population distribution in Colombia. The model's results show that the inclusion of statistical moments improves the accuracy of population distribution predictions. The model was tested using data from the 1985, 1993, and 2005 censuses and projections to 2020. The results show that the model performs well in predicting population distribution, with relative errors below 10% for the first ten statistical moments. The model also shows that the inclusion of statistical moments improves the accuracy of population distribution predictions, especially for low-populated regions. The model's results suggest that information can be considered as an active variable in SES, and that the integration of information, matter, and energy flows is essential for understanding population dynamics in social-ecological systems. The model has potential applications in regional and national management, including resource management and regional planning.