International Journal of Scientific Research and Engineering Development

This paper presents ”Eternal Union,” an AI-driven matchmaking framework designed to enhance marital stability through generation-specific compatibility analysis. Unlike conventional platforms employing static algorithms, the proposed system implements a dynamic logic framework that segments users into three generational cohorts—Gen Z, Millennials, and Divorced individuals—to enable personalized psychological profiling. The architecture integrates a Convolutional Neural Network for realtime age verification and trust-score analysis, establishing a security layer that mitigates fraudulent identities through automated facial age estimation with a ±5-year verification threshold. Marital compatibility is operationalized through five weighted stability pillars—Finance, Communication, Family Boundaries, Trust, and Lifestyle—derived from core divorce triggers, with pillar weights dynamically adjusted based on generational regime detection. Preliminary results demonstrate that psychographically segmented matchmaking achieves higher accuracy in longterm compatibility assessment (stability means: 0.742 for Gen Z to 0.889 for Divorced individuals) with verification accuracy exceeding 96.

Shifa Bilal Tamboli, Simeen Phiroj Mulani, Arman Tajuddin Shiakh,"AI-Driven Generational Matchmaking: A Dynamic Logic Framework for Enhancing Marital Stability and Divorce Prevention" International Journal of Scientific Research and Engineering Development, V9(2): Page(2294-2299) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.