Link Recommendation (LR) in complex networks has caught across-the-network interest in social and computer science communities. Numerous networks, such as recommendation systems and social networks (which facilitate user contact), are probabilistic rather than deterministic due to the uncertainty surrounding the presence of links. Evaluating the various measures has frequently been tricky as the Intra-Layer Linkage Graph requires that at least two nodes be in the same layer. Moreover, many existing LR methods mainly operate well on Single-Layer Graphs (SLGs) compared to Multi-Layer Graphs (MLGs) when nodes traverse Multi-Layers in a network of Intra-Layer linkages. Considering this drawback and others, this paper proposes a Multi-Layer Stochastic Block Interaction method driven by Logistic Regression (MLSBI-LR) to exploit the bi-directional resources associated with Intra-Layer linkages is inherent dependence on Knowledge-based systems use multi-criteria recommender systems to accommodate additional criteria and can modify neighborhood-based approaches. Neighborhood approaches are used since they are more well-accepted and have available experimental data set in MLGs toward recommending links that would efficiently enrich users’ experience. The accuracy and robustness of the proposed MLSBI-LR method compared to existing LR methods were extensively investigated using three distinct benchmark data sets and four evaluation metrics. Based on experimental results across the databases and metrics, the proposed MLSBI-LR method performed significantly better (recording up to 17% increment in accuracy), recommending potential links in MLGs. Consequently, the proposed method may revolutionize link recommendation tasks in social networks by maximizing users’ overall experience