Restricted Boltzmann Machines (RBMs) are a foundational research area in deep learning and probabilistic modeling, focusing on energy-based models that learn a joint probability distribution over visible and hidden units. Research papers in this domain explore RBMs for applications such as dimensionality reduction, feature extraction, collaborative filtering, anomaly detection, speech recognition, image modeling, and IoT data analytics. Key contributions include training strategies like contrastive divergence, hybrid architectures combining RBMs with deep belief networks (DBNs) or deep Boltzmann machines (DBMs), and adaptations for continuous, binary, and categorical data. Recent studies also address challenges such as training stability, convergence efficiency, scalability for large datasets, and deployment on resource-constrained environments. By leveraging RBMs, research aims to extract meaningful latent representations, support generative modeling, and enhance predictive performance across diverse domains.