Interstitial cystitis/bladder pain syndrome (IC/BPS) is a debilitating chronic condition characterized by pelvic pain and urinary disturbances, making its diagnosis challenging due to overlapping symptoms with various lower urinary tract disorders. Current diagnostic methods primarily rely on subjective evaluations, leading to significant difficulties in accurately identifying and managing IC/BPS. This review explores the potential of clustering different disease biomarkers to enhance diagnostic precision for IC/BPS. We examine current research identifying non-invasive biomarkers, including inflammatory markers, oxidative stress indicators, and neurogenic growth factors, while emphasizing the limitations inherent in single biomarker approaches. By employing cluster analysis, which integrates diverse biomarker data relevant to IC/BPS, current studies indicate that this integration facilitates improved sensitivity and specificity in diagnosis while enabling personalized treatment strategies. Emerging machine learning techniques further enhance this analytical framework, identifying predictive urinary biomarkers that can assist frontline clinicians in making informed diagnostic decisions. This review also highlights the need for standardized protocols in biomarker collection and analysis, advocating for multi-omics integration and longitudinal studies to uncover disease mechanisms and improve clinical practices. Ultimately, utilizing a cluster of disease biomarkers aims to provide objective diagnostic tools, thereby supporting clinicians in delivering timely and effective interventions that can significantly impact patients' quality of life.