Organ-on-a-chip models for cardiac tissue are crucial for advancing biomedical research by providing human-relevant data, enabling real-time monitoring, reducing reliance on animal models, and facilitating personalized medicine and high-throughput drug screening. The evolution of the Biowire heart-on-a-chip system for proteomics analysis has significantly advanced our understanding of myocardial fibrosis and other cardiac pathologies. The initial strategy employed Q Exactive HF orbitrap nano LC mass spectrometry for comprehensive proteomic profiling across various cardiac models, including the Biowire system and patient-derived cardiac tissues. By mapping the relative abundance of proteins, key signaling pathways driving fibrosis were identified, offering new insights and a platform for anti-fibrotic drug screening. Additionally, the application of the Biowire system to model Angiotensin II-induced cardiomyopathy highlighted the limitations of rodent models and presented a controllable, human-relevant platform for drug evaluation. This model captured acute and chronic responses to Ang II, facilitating the assessment of cardioprotective compounds such as losartan, relaxin, and saracatinib. Finally, innovations in the Biowire system, incorporating human embryonic stem cell (hESC)-derived macrophages, defined critical interactions in cardiac microtissues, enhancing maturation and function through proteomic analyses. These advancements underscored the role of macrophages in reducing cytotoxicity and promoting cardiomyocyte maturation, with significant implications for in vitro studies and in vivo cell-based therapies. Together, these studies demonstrate the powerful capabilities of Biowire proteomics and organ-on-a-chip systems in advancing cardiac research, providing robust platforms for drug screening and disease modeling. Here, we compare the evolution over the years of the Biowire proteomic technical approaches utilizing isobaric labeling, label free data dependent and data independent acquisition methodologies and their performance in regard to depth of proteomic coverage, dynamic range, technical and biological reproducibility, robustness of bioinformatic and statistical analyses, and integration of findings into biological pathways and networks.