The multi-faceted crime of human trafficking has long afflicted India, and particularly vulnerable to the plight of Indian citizens. The study is designed to resemble an operational expedition and aims at identifying the connection between human trafficking and noteworthy festivals in various Indian states. In order to decipher and analyze the events that take place during these festivals, we use several machine learning algorithms and focus on key leaves in each state to identify hidden links between cultural fests and vulnerable people's victimization. This proposal helps to identify latent issues and provides detailed explanations for their occurrence during festivals. We concentrate on the pivotal leaves in each state, aiming to find sheltered connections between our cultural fests and the exploitation of vulnerable people. Our methods assist in identifying data patterns, just as they would in unravelling a mystery like festive mortal trafficking. This study creates a model to assess the risk of human trafficking in specific regions or populations to help law enforcement and NGOs guide targeted interventions by identifying trends and signs in past data. By highlighting high-risk areas and promoting community engagement in fighting human trafficking, the prediction model raises awareness. A framework for model refining and adapting will be created in this study to ensure forecast accuracy when new data becomes available. The machine learning algorithms detected human trafficking trends on Indian data with high Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, and R2 scores, while Random Forest Regressor performed best with 0.802.