The research focused on elucidating potential targets for breast cancer therapy through the empirical approach to biological networks involved in disease progression. The study was initiated by collecting targets of FDA-approved small-molecule drugs for breast cancer, specifically focusing on those belonging to the kinase inhibitor class. The Swiss Target database was employed to select the kinase inhibitor targets. These targets are then merged and used to construct a network using the String database, which is visualized in Cytoscape. The network analysis is performed using the Cytohubba plugin to identify the top 10 hubgenes in the network. The analysis revealed several high-ranking hub genes, including well-known regulators of breast cancer like EGFR, ERBB2, AKT1, MAPK1, and biomarkers such as JAK1, JAK3, and PRKCD thereby validating the network's reliability. Furthermore, a drug-target analysis was conducted to identify candidate inhibitors for the identified hubgenes. Selection criteria include kinase-specificity, as well as safety and efficacy profiles. Molecular docking was employed to evaluate the binding affinity of MAPK1 with Tucatinib, a protein kinase inhibitor, revealing a strong interaction having a binding affinity of -9.6 kcal/mol. Molecular dynamics (MD) simulations are then performed to assess the stability and dynamics of the MAPK1-Tucatinib complex. The results provide insights into the initial binding conformation and interactions between the protein and ligand. The constructed protein-protein interaction networks offer valuable information on the crosstalk and network interactions of Protein Kinase (PK) within breast oncosystems, while hub genes offer significant targets for interventions in personalized medicine in breast cancer treatment.