Breast cancer is one of the most lethal and common cancers worldwide. Despite the fact that over 100 million putatively pharmacologically active anticancer compounds have been synthesized in private as well as public laboratories, based on estimates, their anticancer activity remains to be stringently validated. The reason is that traditional drug discovery approaches are wasteful, time-consuming, and laborious, not to mention complex. Additionally, the failure rates of newly discovered drugs remain on the increase. Therefore, interdisciplinary approaches are becoming more prominent. These multidisciplinary approaches include computational modeling, molecular dynamics (MD) simulations, density functional theory (DFT), and machine learning (ML). Obviously, these combinatorial approaches enable scientists to predict the efficiency of rationally designed compounds with potential anticancer activity and choose the most promising candidates for synthesis and experimental confirmation. Over the past decade, the use of nanomedicine for the treatment of breast cancer has gained considerable interest. Nanomedicines are more soluble and stable, and they can travel more readily through the bloodstream to reach their target—tumor cells. This chapter will first examine the application of computational chemistry, including DFT-based studies and MD simulations, in predicting cancer-related mechanisms and in the design of promising anticancer candidates. Subsequently, the chapter will explore how interdisciplinary strategies can be leveraged to design novel nanomedicine-based drug delivery systems. The discussion in the current chapter will reveal why the advancement in various scientific fields, such as quantum mechanical studies, which provide details about interactions at microstates, molecular dynamic simulations, which enable the researchers to simulate the cellular environment in detail, and machine-learning models, which enable the scientists to gather a large dataset of existing anticancer drugs and effectively compare them with each other and with their newly designed anticancer compounds, are needed along with the experimental synthesis and testing of the newly designed anticancer drugs. This chapter manifests that the goal of interdisciplinary approaches is to complement the traditional ones by increasing the efficacy of the traditional approaches and decreasing their complexity and workload for drug discovery.