Bayesian Networks are increasingly being applied for real-world data problems. They provide the much desired complexity in representing the uncertainty of the predicted results of a model. The networks are easy to follow and better understand the inter-relationships of the different attributes of the dataset. As part of this talk, we will look into the existing R and Python packages that enable BN learning and prediction. The pros and cons of the available packages will be discussed as well as new capabilities that will broaden the application of BN networks.