Micro RNA or miRNA is a highly conserved class of non-coding RNA that plays an important role in many diseases. Identifying miRNA-disease associations can pave the way for better clinical diagnosis and finding potential drug targets. We propose a biologically-motivated data-driven approach for the miRNA-disease association prediction, which overcomes the data scarcity problem by exploiting information from multiple data sources. The key idea is to enrich the existing miRNA/disease-protein-coding gene (PCG) associations via a Message Passing framework, followed by the use of disease ontology information for further feature filtering. The enriched and filtered PCG associations are then used to construct the interconnected miRNA-PCG-disease network to train a structural deep network embedding (SDNE) model. Finally, the pre-trained embedding and the biologically relevant features from the miRNA family and disease semantic similarity are concatenated to form the pair input representation to a Random Forest classifier whose task is to predict the miRNA-disease association probability. We present large-scale comparative experiments, ablation, and case studies to showcase our approach’s superiority. Besides, we make the model prediction results for 1,618 miRNAs and 3,679 diseases, along with all related information publicly available at http://software.mpm.leibniz-ai-lab.de/index.html to foster assessments and future adoption.