Mr. Ramu V, Dr. M.Vinoth Kumar 05 05 Bayesian Deep Learning has emerged as a powerful framework for modellinguncertainty in deep neural networks In traditional deep learning models are often treated asdeterministic providing point estimates for predictions However in many realworldapplications it is crucial to quantify uncertainty especially when dealing with limited datanoisy measurements or safetycritical systems This paper provides an overview of BayesianDeep Learning and its applications for uncertainty estimation We explore the foundationalconcepts methodologies and practical techniques for incorporating Bayesian principles intodeep neural networks Key topics covered include probabilistic modelling Bayesian neuralnetworks variational inference and Monte Carlo dropout We discuss how Bayesian DeepLearning can be applied to various domains including computer vision natural languageprocessing reinforcement learning and autonomous systems The advantages and challengesof uncertainty estimation in these applications are highlighted Furthermore we review recentdevelopments and open research directions in Bayesian Deep Learning such as scalableBayesian models uncertaintyaware active learning and model compression Theseadvancements are driving the integration of Bayesian principles into the mainstream ofmachine learning enabling more robust and reliable decisionmaking in AI systems Overallthis paper serves as a comprehensive introduction to Bayesian Deep Learning emphasizingits significance in addressing uncertainty in modern machine learning and it provides aroadmap for researchers and practitioners interested in harnessing the power of uncertaintyaware AI systems Copyright (c) World Journal of Pharmaceutical Seiences. All rights reserved 2026