This work applied three machine learning (ML) models—linear regression (LR), random
forest (RF), and support vector regression (SVR)—to predict the lattice parameters of the monoclinic
B19′ phase in two distinct training datasets: previously published ZrO2-based shape-memory ceramics (SMCs) and NiTi-based high-entropy shape-memory alloys (HESMAs). Our findings showed
that LR provided the most accurate predictions for ac, am, bm, and cm in NiTi-based HESMAs, while
RF excelled in computing βm for both datasets. SVR disclosed the largest deviation between the
predicted and actual values of lattice parameters for both training datasets. A combination approach
of RF and LR models enhanced the accuracy of predicting lattice parameters of martensitic phases in
various shape-memory materials for stable high-temperature applications.