The application of wind power within smart grids is crucial thereby mandating integrative energy change. Yet, the randomness of wind and technological nature of wind turbines lead to quite a number of problems regarding the dependability and performance of supplied energy. These issues can be managed through the help of predictive maintenance which reduces the time that is taken in the machinery’s periodic maintenance while at the same time reduces the cost of the overall maintenance. The following research work puts forward a framework for developing deep learning models to improve the approaches to maintain the wind energy systems using LSTM networks. The framework is based on the Wind Turbine SCADA Dataset which is a collection of time-series of various parameters of the turbine like wind speed, power output, rotor speed, temperature, and vibration and aims at predicting the probable equipment failures in future. The LSTM network used for the diagnosis of mechanical problems learns from the sensor data and watches for anomalies in real-time. Thus, owing to the organization of the maintenance activities according to such predictions, the framework minimizes the unexpected downtimes, and increases the lifespan of the turbine components, as well as the reliability of the energy production. In addition, the proposed business incorporates the predictive maintenance system into the smart grid system to facilitate energy management and the synchronization of the maintenance system with grid functionality. The research also responds to important issues: the fluctuation in the wind energy data and the adaptability of an LSTM model to large power stations.