A processor-implemented method comprising: obtaining, by one or more hardware processors, from a data lake a plurality of datasets associated with a topic, wherein each of the plurality of datasets comprise information corresponding to various attributes of the topic; joining, by the one or more hardware processors, the plurality of datasets to obtain a joined dataset for learning correlations among the datasets; and predicting, by the one or more hardware processors, distribution based on Bayesian modelling, wherein the distribution is associated with a target attribute by selecting, by the one or more hardware processors, a plurality of attributes based on mutual information with the target attribute in the joined dataset; learning, by the one or more hardware processors, a minimum spanning tree-based Bayesian structure on a feature graph (300A, 300B) that is created by calculating pairwise mutual information between selected attributes and the target attribute; using Bayesian networks for imputing, by the one or more hardware processors, attributes that are part of a Bayesian network in datasets where they are partially missing; learning, by the one or more hardware processors, conditional probabilistic tables at each node of the minimum spanning tree based Bayesian structure; querying, by the one or more hardware processors, the conditional probabilistic tables for predicting the distribution associated with the target attribute; predicting, by the one or more hardware processors, the distribution associated with the target attribute by querying the conditional probabilistic tables, thereby facilitating Bayesian data fusion by automatically generating a plurality of tags to index files. obtaining, by one or more hardware processors, from a data lake a plurality of datasets associated with a topic, wherein each of the plurality of datasets comprise information corresponding to various attributes of the topic; joining, by the one or more hardware processors, the plurality of datasets to obtain a joined dataset for learning correlations among the datasets; and predicting, by the one or more hardware processors, distribution based on Bayesian modelling, wherein the distribution is associated with a target attribute by selecting, by the one or more hardware processors, a plurality of attributes based on mutual information with the target attribute in the joined dataset; learning, by the one or more hardware processors, a minimum spanning tree-based Bayesian structure on a feature graph (300A, 300B) that is created by calculating pairwise mutual information between selected attributes and the target attribute; using Bayesian networks for imputing, by the one or more hardware processors, attributes that are part of a Bayesian network in datasets where they are partially missing; learning, by the one or more hardware processors, conditional probabilistic tables at each node of the minimum spanning tree based Bayesian structure; querying, by the one or more hardware processors, the conditional probabilistic tables for predicting the distribution associated with the target attribute; predicting, by the one or more hardware processors, the distribution associated with the target attribute by querying the conditional probabilistic tables, thereby facilitating Bayesian data fusion by automatically generating a plurality of tags to index files.