Transportation of vehicles emit carbon dioxide which contributes to global warming and ozone layer depletion. Fossil fuel powered vehicles are being replaced with electric vehicles. Electric vehicles are becoming more popular in many nations, but the issues are unaffordable prices, poor infrastructure, and too few charging stations. Due to electric car popularity, charging stations are scarce. These concerns grow over time. People cannot afford electric cars due to a lack of charging outlets. So Integrated Charging stations (ICS) with a renewable energy source will be suitable to meet the demand of the current scenario. Standalone and sole source of renewable energy are highly uncertain to the weather conditions. The main purpose of hybrid energy was to lessen distribution network load. These hybrid power sources include solar panels, wind turbines, and batteries (PTBS). Hybrid sources research uses real-world wind speed and radiation data combined with battery performance data given to the IOT device to improve hybrid system reliability and lower costs. Hybrid renewable power systems must prepare for weather uncertainty. The data collected from IOT will be fed to the Feed Forward Neural network (FFNN) for the power management and control over the supply to charging stations. The FFFN method is employed to compare the available data, the current data received to and state the nature of the demand on both the source as well as charging station whether the power have to be released or stored. By this methodology, the efficiency of the system is increased to 96.5% in renewable energy usage, 96.4% in battery storage usage and decreased to 3.5% in energy loss to make this system more effective on power management for the balanced transfer of energy.