Collapse of a lyophilized cake is a severe failure mode for biologic products because it compromises structure, appearance, and downstream usability. Collapse temperature is difficult to measure experimentally and is governed by interacting formulation and process variables. Formulation scientists lack a rapid quantitative tool for predicting collapse temperature from routinely collected development information. Current decisions often rely on empirical rules, limited thermal measurements, and small numbers of freeze-drying microscopy observations. The objective of this predictive model article is to describe a machine learning framework for estimating collapse temperature in lyophilized biologic formulations. The model is intended to use formulation composition, thermal analysis, freezing-process descriptors, and residual-moisture information. A gradient-boosted regression framework is proposed for learning relationships among protein concentration, excipient composition, glass-transition behavior, cooling history, annealing conditions, and residual moisture. The workflow emphasizes curated inputs, physicochemically meaningful feature engineering, uncertainty-aware prediction, and interpretability. Conceptually, the model would be expected to support collapse-temperature prediction across protein–excipient combinations without replacing experimental confirmation. It could rank formulation variables by their influence on collapse risk and guide in-silico screening before targeted freeze-drying microscopy. A predictive collapse-temperature tool could accelerate biologic lyophilization development by connecting formulation design with process-risk assessment. Such a model is aligned with quality-by-design thinking because it converts scattered development observations into actionable formulation and cycle-design knowledge.