In the present study, sustainable development is investigated in Iran using renewable energies-assisted Techno-Econo-Socio-Environmental Multivariate Analysis (TESEMA) as a novel holistic approach. Accordingly, six annual hourly consumption variables, reported by Iran's power industry from 2011 to 2017, are predicted using seven dynamic and intelligent models. Consequently, technical and economic variables are obtained by an optimal design of hybrid solar, wind, and biogas systems at 53 sites in Iran. Thirteen social variables are studied using a technique for order-preference by similarity to an ideal solution (TOPSIS) and six hazardous air pollutants are reported in Iran using a geographic information systems interpolation tool. Then, four major TESEMA variables are used in multivariate statistical analyses to reduce the big data diversity. Principal component analysis (PCA) is performed to find a linear model among the variables, and K nearest neighborhood (KNN) algorithm is used to cluster the sites according to the modeling results. A partial least square-based regression is conducted to investigate any correlation between major variables of TESEMA and population density in Iran. Finally, TESEMA development index (DI) and facial graphs are proposed as novel numerical and graphical sustainable development monitoring techniques, respectively. The results show that DNN is the best model to predict demand load in Iran (RMSE = 73.15%). Since DI varies in a wide range from 0 to 248.83 and the population density is significantly correlated with TESEMA variables (R-2 = 91.86%), the current centralistic policies should be revised in Iran to reach sustainable development. Thus, a four-cluster management strategy accompanied by smart monitoring can efficiently lead to sustainable development in Iran.