Since the nineteenth century, cancer has been treated mainly by surgery, radiation, and chemotherapy. However, limitations of these modalities, like the inability to surgically resect risky anatomical positions, side effects of radiation, and non-specificity of chemotherapy, have hindered clinicians from achieving optimal results. Two patients with the same type and grade of tumor were found to respond differently to the same chemotherapy. The reason behind some patients achieving meaningful results while others had no effect was investigated to be due to the uniqueness of each individual’s molecular code, immunological landscape, lifestyle habits, and/or genetic makeup. Since we aim to deliver maximum benefit to all patients, there came the need to precisely evaluate each patient’s tumor in terms of genomics, transcriptomics, proteomics, pharmacogenomics, metabolomics, and epigenomics to find out key vulnerabilities that could be targeted to achieve better outcomes compared to the “one size fits all” model of traditional therapy. Currently, researchers, clinicians, and bioinformaticians focus mainly on the genomics of individual tumors and design treatment regimens or clinical trials. So far, many such treatment strategies and clinical trials have achieved improved progression-free survival and overall response rates compared to standard first-line chemotherapy. However, challenges like high-cost sequencing platforms, availability of skilled data analysts, and socioeconomic inequities have slowed down the pace of precision oncology to become the mainstay in clinical settings.