Purpose: Transcriptomics has been revolutionized by the development of microarray technology, which makes it possible to simultaneously measure thousands of genes' levels of gene expression. This innovation holds an immense potential in understanding cardiovascular diseases such as Ischemic Cardiomyopathy (ICM) and Non-Ischemic Cardiomyopathy (NICM), which present substantial health concerns on a global scale implying the need for studying ICM and NICM exhaustively. The primary objective of this proof-of-concept paper aims at uncovering potential biomarkers and learn using data-driven method to identify important genes that are differentially expressed. Methods: Microarray data from Gene Expression Omnibus (GEO) repository provided the dataset, which includes expression data from peripheral blood mononuclear cells (PBMC) of patients with ischemic and non-ischemic cardiomyopathy as well as a control group that was age and gender-matched. This research paper endeavours to conduct comprehensive microarray data analysis for transcriptomic profiling aimed at the identification of differentially expressed genes (DEG) associated with cardiomyopathy. Leveraging a data science process model, this study delves into the exploration and interpretation of a specific dataset, GDS3115, curated for its relevance to cardiomyopathy. Results: In total, five DEG showing significant differences in their Gene Expression Profiles to make diagnostic / prognostic analysis were identified. The inferences are tabulated and plotted the DEG in volcano plot as an interpretation of result obtained. Conclusion: Candidate biomarker genes such as CX3CR1C, HSPA1L///HSPA1B///HSPA1A, JUN, ZNF331, RORA are ICM’s therapeutic targets. This study identified several DEG that may be involved in the pathogenesis of ICM/NICM. This abstract synthesizes the research idea, workflow, methodologies employed, and the potential implications of the study in identifying cardiomyopathy related genes via Biological analysis using the GDS3115 dataset.