Rapid laser scanning generates a complex heat-affected zone with steep temperatureTemperature gradients in laser additive manufacturingAdditive manufacturing, including laser metal deposition (LMD) and laser powder bed fusion process (LPBF). The complex thermal history and severe gradients lead to very high thermal stressesThermal stress that evolve into residual stressesResidual stress after the component cools down. Data-driven methods, such as machine learningMachine learning (ML), offer an alternative to traditional physics-based simulationsSimulation for calculating the thermal stressThermal stress evolution. However, ML often requires a large, labeled training dataset, which is computationally inefficient. In addition, the “black box” nature of data-driven ML methods makes it difficult to interpret the results. Additionally, data-driven ML methods do not use governing physical laws underpinning laser additive manufacturingAdditive manufacturing to make them data-efficient. This study aims to develop a physics-informed ML (PIML) model that can predict thermal stressesThermal stress during laser scanning without requiring any labeled training dataset. A case study has been conducted to demonstrate the predictive capability of the PIML method and examine the evolution ofPhysics-informed machine learning thermal stressesThermal stress in an LMD process.