A comprehensive analysis of deep learning methods utilized in Human Activity Recognition (HAR) across multiple domains such as healthcare, security, and sports were discussed in this article. Deep learning (DL) techniques have shown significant advancements over traditional machine learning (ML) approaches in terms of accuracy and resilience. The study explores various DL models including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) models, as well as their combinations. It discusses the architectures, optimization methods, and preprocessing techniques employed to enhance the effectiveness of DL-based HAR systems, such as data augmentation, feature extraction, and dimensionality reduction. The article also highlights datasets and performance metrics utilized for evaluating the efficacy of HAR systems. Furthermore, it addresses current challenges and future prospects in deep learning-driven research for HAR, including the need for improved interpretability, adaptability to novel activities and settings, and integration of multiple modalities to advance HAR. In summary, this review provides valuable insights into DL-driven human activity recognition systems, culminating in a maximum performance of 96.8% achieved by a multi-stream CNN combined with LSTM.