All-solid-state lithium metal batteries (ASSBs) are promising candidates for next-generation energy storage but face significant challenges from unpredictable short-circuit failures [1]. Experimental observations reveal that critical current densities (CCD) often vary by orders of magnitude (0.03–1.0 mA cm⁻²) even among nominally identical samples. This stochasticity follows a Weibull distribution, indicating that failure is not governed by a single deterministic threshold but is a statistical extreme event driven by the "weakest link" in the microscopic defect landscape [2]. Current understanding of lithium propagation typically diverges into two parallel paths: the mechanical fracture theory which attributes failure to stress-driven crack propagation [3-5], and the electronic leakage model which emphasizes isolated lithium nucleation due to local electronic structure anomalies [6,7]. Bridging these perspectives, we propose a panoramic electro-chemo-mechanical framework that explicitly couples grain boundary electronic leakage with electrochemical-mechanical dynamics. Our model uncovers a "Kinetic Mismatch Mechanism" where brittle crack propagation significantly outpaces viscoplastic lithium filling. This rate disparity creates a transient "dry crack" window that temporally decouples mechanical damage from electrical breakdown. Consequently, the topological arrangement of the defect network—rather than macroscopic porosity—becomes the decisive factor in determining whether these dry cracks evolve into conductive short-circuit pathways. By integrating this framework with interpretable machine learning (SISSO) [8] to screen tens of thousands of candidate features, we identify critical topological descriptors (e.g., local connectivity and maximum cluster size) that govern battery lifetime. Furthermore, we establish a "Dominance Phase Map" that successfully decouples the competitive contributions of macroscopic density and microscopic topology. This work resolves the paradox of stochastic failures in high-density electrolytes and provides quantitative topological invariants for designing high-robustness solid electrolytes.