Deep Boltzmann Machines (DBMs) are a specialized research area in deep learning, focusing on probabilistic generative models with multiple layers of stochastic hidden units capable of capturing complex data distributions. Research papers in this domain explore DBMs for applications such as image recognition, speech processing, anomaly detection, feature learning, IoT data analytics, and recommender systems. Key contributions include unsupervised pretraining, approximate inference methods for efficient learning, hybrid models combining DBMs with convolutional or recurrent architectures, and strategies for overcoming challenges like training complexity, convergence issues, and scalability to large datasets. Recent studies also investigate energy-efficient implementations for resource-constrained devices, applications in healthcare and finance, and integration with transfer learning or semi-supervised learning frameworks. By leveraging DBMs, research aims to model high-dimensional and complex data distributions, enabling robust feature extraction, generative modeling, and improved predictive performance across diverse domains.