Tin oxide semiconducting material has been emerged as attractive active thin-layer alternative for developing high-performance and low-cost microelectronic devices. The effects of oxygen concentration and growth technique during the deposition process on the electrical properties of SnO_x alloy should be investigated for developing new eco-friendly photosensors and photovoltaic devices. The present work aims to predict the electrical key governing parameters throughout the device developing processes such as the Energy level values and band-gap energy as function of the injected oxygen concentrations. For realization, over 1000 data points were collected by modeling the effect of oxygen contents on the SnO_x electrical properties using Density Function Theory (DFT). Through extensive Machine Learning (ML) analysis, the impact of the oxygen concentration on the electrical properties and the material type is well predicted, where the applied ML prediction model for band-gap energy showed a good correlation between predicted values and the calculated ones using DFT computations. It is revealed that the combined DFT-ML-based approach can be robust, accurate and easy-to-implement tool to study and accelerate the developing of new highly efficient materials for microelectronic applications