Compressive strength is the critical quality metric in industrial cement production, yet conventional assessment relies on destructive 28-day tests or inaccurate accelerated methods, hindering timely quality control. These methods inherently cause sample scarcity (only 52–195 batches/year/plant) due to the 28-day curing requirement, labor-intensive specimen preparation, and destructive testing protocols—rendering them insufficient for data-driven models requiring large samples. Concurrently, plants face partial observability with arbitrary critical features (e.g., chemical composition) missing due to sensor limitations and prohibitive assay costs. While machine learning models accelerate prediction, their deterministic single-value outputs fail to infer missing features under partial observability and require extensive samples unattainable under scarcity, causing significant compliance risks and overdesign costs. We resolve these by reframing strength prediction as a probability density estimation task, replacing single-value estimates with full probability densities. Our proposed cement strength density estimator (CSDE): first, outputs strength as Gaussian mixture densities, second, resolves partial observability via latent variable inference, and third, overcomes sample scarcity through likelihood optimization. Validated on industrial data under partial observability and scarcity, CSDE exhibits only 2.78% MAE degradation under 82% feature masking (versus 3.73% for MLPs) and achieves 97% of strengths within 95% confidence intervals. By converting densities into compliance metrics [P(strength ≥\geq critical value)], CSDE flags high-risk batches, reduces overdesign costs, and supports timely quality interventions. The framework is extensible to flexural/tensile strength prediction via input redefinition.