Child-related criminal offenses are amongst the utmost heinous, targeting the vulnerable youth of the society. These crimes include physical & sexual abuse of children, child labor, child trafficking, cyberbullying etc. The World Health Organization (WHO) estimates that up to 1 billion crimes against children have occurred globally. In India alone, more than 350 such crimes are reported each day. This study focuses on identifying crime hotspots related to children in India using hierarchical clustering, a machine learning technique. The research utilized crime data from the National Crime Records Bureau (NCRB) India for 2016–2020, alongside state-wise child population estimates, to group states according to the gravity of offenses committed against children. The data was pre-processed, normalized, and analyzed using KNIME software, which applied a bottom-up hierarchical clustering approach to create a dendrogram for visualizing crime clusters. The results revealed three category Indian states of crime zones in India: high, medium, and low. Delhi state was identified as the primary hotspot with a very high crime rate, followed by 17 states in the medium-risk category, and 12 states in the low-risk category. These findings underscore the need for targeted interventions and enhanced child protection policies, especially in Delhi. The study illustrates how hierarchical clustering can be effectively applied to criminology for identifying high-risk areas and informing policy decisions. Future research may include the use of localized data and exploration of other clustering algorithms to refine and improve crime analysis and prevention strategies.