• Ayana Lala
• Bhumika Tekam
• Jyoti Bhowmick
• Archana Singh
• Dr. Shilpa Bhalerao, Dr. Vandana Kate, Prof. Chanchal Bansal

Digital matchmaking platforms often fail to address the accessibility, emotional compatibility, and safety needs of individuals with disabilities. To bridge this gap, this study proposes an AI-powered matrimonial platform that integrates Natural Language Processing (NLP) and Gradient Boosting Machine (GBM) for intelligent and personalized matchmaking. The proposed system utilizes NLP to interpret emotional cues and user preferences, while GBM optimizes compatibility scoring to ensure matches are both data-driven and emotionally aligned. Key innovations include accessibility features (text-to-speech, voice command support) and a Parental Vigilance module, enabling guardians to monitor profile interactions—enhancing safety for users requiring assistance. The framework advances digital matchmaking by combining AI-driven compatibility analysis with inclusive design principles, fostering a secure and equitable experience for individuals with diverse needs. This research demonstrates how machine learning can transform matchmaking into a more empathetic, accessible, and trustworthy process for underserved populations.

Natural Language Processing (NLP), Gradient Boosting Machine (GBM),Compatibility coring, Parental Vigilance, Assistive Technology, Emotional Intelligence, Inclusive Matchmaking, Accessibility.

"Intelligent Matchmaking for Diverse Needs Using NLP and Gradient Boosting for Inclusive Pairing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.243-250, May-2025, Available :http://www.jetir.org/papers/JETIRGV06035.pdf

"Intelligent Matchmaking for Diverse Needs Using NLP and Gradient Boosting for Inclusive Pairing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. pp243-250, May-2025, Available at : http://www.jetir.org/papers/JETIRGV06035.pdf