<p>This study introduces a novel methodology for quantifying strain-induced martensite in austenitic TRIP steels using magnetic flux density measurements from a Teslameter as the primary physical quantity. Incremental tensile tests combined with XRD and microstructural analysis established the correlation between martensite fraction and magnetic flux density. To model this nonlinear relationship, three formulations were developed and optimized: a proposed empirical model, the physically based Olson–Cohen (O-C) model, and a modified Avrami (M-A) model. A multi-algorithm machine learning approach was employed for calibration, comparing deterministic Levenberg-Marquardt with metaheuristics including genetic algorithm, particle swarm optimization, and grey wolf optimizer in Python. The empirical model achieved the highest fitting accuracy (MSE: 0.5919, <i>R</i>²: 0.9978), while the O-C model demonstrated superior algorithmic stability with all optimizers converging identically. Results reveal a fundamental trade-off between empirical accuracy and physical consistency of transformation kinetics. This work enables reliable, data-driven quantification of martensitic evolution through non-destructive magnetic evaluation, facilitating real-time structural condition monitoring of TRIP steel components.</p>