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Mastitis, a disease with high incidence worldwide, is the most prevalent and costly disease in the dairy industry. Gram-negative bacteria such as <i>Escherichia coli</i> (<i>E. coli</i>) are assumed to be among the leading agents causing acute severe infection with clinical signs. <i>E. Coli</i>, environmental mastitis pathogens, are the primary etiological agents of bovine mastitis in well-managed dairy farms. Response to <i>E. Coli</i> infection has a complex pattern affected by genetic and environmental parameters. On the other hand, the efficacy of antibiotics and/or anti-inflammatory treatment in <i>E. coli</i> mastitis is still a topic of scientific debate, and studies on the treatment of clinical cases show conflicting results. Unraveling the bio-signature of mastitis in dairy cattle can open new avenues for drug repurposing. In the current research, a novel, semi-supervised heterogeneous label propagation algorithm named Heter-LP, which applies both local and global network features for data integration, was used to potentially identify novel therapeutic avenues for the treatment of <i>E. coli</i> mastitis. Online data repositories relevant to known diseases, drugs, and gene targets, along with other specialized biological information for <i>E. coli</i> mastitis, including critical genes with robust bio-signatures, drugs, and related disorders, were used as input data for analysis with the Heter-LP algorithm. Our research identified novel drugs such as Glibenclamide, Ipratropium, Salbutamol, and Carbidopa as possible therapeutics that could be used against <i>E. coli</i> mastitis. Predicted relationships can be used by pharmaceutical scientists or veterinarians to find commercially efficacious medicines or a combination of two or more active compounds to treat this infectious disease.