Cancer is the second most common cause of fatalities associated with non-communicable diseases across the globe, affecting multiple organs and often necessitating costly treatments with adverse effects. Pharmacology examines the interactions between drugs and biological systems, encompassing pharmacodynamics, pharmacokinetics, and the development of personalized medicine. Network pharmacology integrates systems biology and computational tools to explore multi-target drug mechanisms, enhancing traditional medicine insights, biomarker identification, and personalized treatment development for complex diseases. By integrating pharmacology with systems biology, the network pharmacology (NP) approach emerged as a pivotal technique to uncover the potential mechanisms driving the therapeutic actions. Similarly, Ashwagandha (Withania somnifera), a key herb in Ayurveda, is renowned for its versatile medicinal benefits. It is traditionally used as an aphrodisiac, liver tonic, anti-inflammatory agent, and antioxidant, while also enhancing memory and stress resilience. The withanolides, rich in oxygen, exhibit notable anticancer potential, rendering the plant effective against various health conditions. The present study aims to integrate NP and molecular modeling studies to identify Ashwagandha-derived phytochemical-based candidate inhibitors of breast cancer-related proteins. A comprehensive analysis identified 30 active compounds in W. somnifera, 157 potential cancer targets of the plant, and 14,174 breast cancer-related targets from various databases. Network analysis identified AKT1, EGFR, JUN, STAT3, RELA, MAPK14, PIK3R1, and MYC as key targets associated with breast cancer. GO (Gene Ontology) and Pathway enrichment analysis identified MAPK14 as a promising therapeutic target for the present study. Among all the analyzed molecules, Viscosalactone B (-9.4 kcal/mol towards MAPK14) and Withasomniferol C (-9.73 kcal/mol towards MAPK14) emerged as the most promising lead candidates, showing consistent binding affinity, structural stability, lower flexibility, enhanced compactness, and more favorable dynamic behavior. These two lead candidates stabilized MAPK14, as evidenced by reduced RMSD (0.394 and 0.318 nm) and compact Rg values (2.230 nm each). Additionally, both compounds exhibited lower PCA trace values of the covariance matrix (307.487 and 214.386 nm2), indicating restricted conformational flexibility. In addition to MAPK14, the prioritized lead candidates exhibited strong binding affinities towards AKT1 (-8.90 to -9.80 kcal/mol) and STAT3 (-8.93 to -9.50 kcal/mol), highlighting their potential as multi-target inhibitors. Therefore, Viscosalactone B and Withasomniferol C are promising natural candidates for further validation as potential MAPK14 inhibitors. In comparison with synthetic drugs like ralimetinib, these plant-derived compounds may offer complementary therapeutic potential with fewer adverse or off-target effects and favorable pharmacokinetic and pharmacophoric profiles.