Abstract: A system and method of detecting Concept Drift (CD) using a Bayesian Network (BN) model may include: receiving a first version of the BN model, comprising a plurality of interconnected nodes, each node comprising a conditional probability table (CPT) representing statistic characteristics of a corresponding domain variable; computing at least one CPT of at least one node of the first version BN based on an incoming data stream to produce a second version of CPTs representing a second version of the BN; calculating a CPT distance value between at least one first CPT of the first version and at least one corresponding second CPT of the second version; identifying at least one suspected node of the BN as undergoing CD based on the at least one calculated CPT distance value; and producing an optimized version of the BN based on the identification of the suspected node.