Fall recovery is the capability of a person to recover from external forces. It is an inevitable phenomenon in a cluttered environment. To detect the fall, various mechanisms are evolved for elderly subjects, as they are more prone to fall which may lead for permanent disability or death. With the advancement of sensing and computing technology, the IoT-based wearable devices become popular. In this paper, we present an innovative method for early fall detection and monitoring is proposed during various days to day activities. This research proposes the use of a wearable IMU sensor for detecting body movements. The IMU sensor consists of gyroscopes and accelerometers. Scalability and efficiency may be constrained by the labor-intensive feature engineering and human data processing that are key to traditional fall detection techniques. We developed a hybrid deep learning framework that integrates LSTM networks with CNNs to effectively tackle these challenges. This innovative architecture achieves an accuracy of 98.03% on the Smart Human Fall Dataset, successfully identifying both temporal and spatial patterns in movement data. The proposed model has greatly improves the scalability and dependability of fall detection systems by automating feature extraction and streamlining the data processing pipeline. This provides a solid solution for practical applications and encourages older adults to be more independent and safe.