Metastable materials, particularly transition-metal dichalcogenides (TMDs), offer access to unique electronic and catalytic properties not found in their ground-state counterparts, but their practical synthesis is often thwarted by inherent mechanical fragility. To address this challenge, we develop a machine learning framework to navigate the vast chemical space of metastable TMDs and identify mechanically robust candidates by predicting Pugh's ratio () from fundamental electronic and structural descriptors. Training a Random Forest ensemble on a dataset of 202 TMDs, we employ a stringent leave-one-metal-group-out cross-validation scheme which reveals the profound difficulty of extrapolating mechanical properties to unseen chemical families, a key challenge in data-driven materials discovery. Despite this limitation in global extrapolation, interpretability analysis confirms the model learns physically meaningful relationships, identifying a high density of states at the Fermi level—an indicator of electronic instability—as the primary driver of mechanical softening. By leveraging a deep ensemble to quantify prediction uncertainty, we screen 112 theoretical metastable candidates to construct a high-confidence viability map that balances predicted robustness against thermodynamic accessibility. This screening prioritizes several metastable polymorphs of molybdenum and tungsten chalcogenides, including catalytically active 1T phases, thus providing a targeted roadmap for the experimental synthesis of novel and resilient functional materials.