We present the first experimental machine learning (ML)-enabled Process-Technology Co-Optimization (PTCO) framework for optimizing 2D transition metal dichalcogenide (TMD) FET fabrication directly from statistically meaningful experimental data rather than pure simulation data. We first introduce a transition voltage metric, VTrans, to quantify the gate voltage required for off-to-on switching and reveal its direct correlation with subthreshold swing (SS), highlighting an overlooked switching characteristic that governs both off-state and on-state performance. By integrating automated metric extraction, multi-objective recipe ranking, and predictive modeling, our framework uncovers hidden process-performance correlations and predicts the performance of unexplored fabrication recipes from limited experimental data. Experimental validation shows close agreement with ML predictions, thus demonstrating the framework's ability to efficiently guide gate stack optimization through iterative experimental feedback.
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