Ransomware has become a pervasive threat in the cybersecurity landscape, causing significant financial and operational damage across various sectors. Despite substantial research focused on well-known ransomware families, less commonly labeled variants remain underexplored, representing a hidden yet critical facet of the threat spectrum. This research presents a comprehensive analysis of less commonly labeled ransomware families, employing advanced clustering techniques, similarity metrics, and pattern recognition to uncover evolutionary connections and shared characteristics with more prominent ransomware. The study's findings reveal that even lesser-known ransomware can exhibit sophisticated behaviors and adaptation strategies, emphasizing the need for inclusive cybersecurity approaches that encompass the full range of ransomware threats. Through a novel methodological framework, the research highlights the effectiveness of machine learningbased detection techniques in identifying both common and obscure ransomware variants, thus contributing to the development of robust and adaptive cybersecurity defenses. By providing a deeper understanding of ransomware evolution and behavior, the study enhances the ability to anticipate and mitigate future threats, promoting a more resilient cybersecurity environment.