Renewable energy sources have long been established as viable alternatives to fossil-fuel-based resource use. Wind energy is a device that converts mechanical energy into electrical energy. Furthermore, of all renewable energy sources, large-scale wind turbines power generation. IoT data is transformed into useful information in order to enhance wind turbine performance, lowering wind energy costs and lowering risk. However, because the wind turbine system and component levels require real-time control, IoT deployment is a difficult challenge. In this method using IoT to assess wind resources and estimate the lifetime of wind power modules. A model with sub-models of an aerodynamic rotor connected directly to a multi-pole variable speed Permanent Magnet Synchronous Generator (PMSG) with variable speed control, pitch angle control, and full-scale converter connected to the grid is created to highlight this issue. Quality of the force determines the strength of the electrical capacity of the method. The Voltage repetition and stage synchronization allow the electrical system to work in its proposed conduct without execution or extensive loss of life using neural network calculations. Axial Flux Permanent Magnet Synchronous Generation (AFMSG) is allowed to turn the turbine with variable speed, indicating that the force and repetition of the generator shift are constant. The calculation of the neural network algorithm along these lines should be a clear working point and a plan of the generator for full functional reach is controlled by wind speed propagation. It has been tracked that the non-cover winding hinge magnet has generally a good performance for the wind energy system associated with the simultaneous generator.