Predicting the failure and plasticity of solids remains a longstanding challenge, with broad implications for materials design and functional reliability. Disordered solids like metallic glasses can fail either abruptly or gradually without clear precursors, and the mechanical response depends strongly on composition, thermal history, and deformation protocol—impeding generalizable modeling. While deep learning methods offer predictive power, they often rely on numerous input parameters, hindering interpretability, methodology advancement, and practical deployment. We address this longstanding challenge with a macroscopic, physically grounded approach that uses plastic strain accumulation in the elastic regime to robustly predict deformation and yield. This method reduces complexity and improves interpretability, offering a practical alternative for disordered materials. For the Cu-Zr-(Al) metallic glasses prepared with varied annealing, we identify two limiting regimes of plastic strain growth: power law in poorly annealed and exponential in well-annealed samples. A physics-informed framework with Bayesian inference extracts growth parameters from stress-strain data within $\sim 5\%$ strain, enabling early prediction of bulk response and yield point, well before the failure. The predictive performance improves with annealing and bulk plasticity correlates with the microscopic plastic activity from scattered to growth near yielding. This work presents a physically interpretable and experimentally relevant framework for predicting plasticity, stress-strain curve, and failure in metallic glasses from early mechanical response, offering both theoretical insights and practical tools for material characterization and design.

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• Growth processes
• Plasticity
• Metallic glasses
• Bayesian methods
• Machine learning
• Molecular dynamics