During cavitation bubble pulsations, a phase change intensively occurs near the collapsing moment due to high pressure and temperature inside bubbles, accompanying distinctive flow features: the rate of evaporation and condensation significantly changes according to the phase change regime. To account for this non-isothermal effect, the high-fidelity computational framework incorporating the physics-based cavitation model and a new fluid property model based on artificial neural network is proposed. The key finding of this study is the interplay between the thermal and inertial effects during multiple pulsations. At the early stages of bubble contraction, the phase change is primarily driven by fluid inertia. However, as the bubble continues to compress, the thermal effect becomes dominant and controls the entire phase change region at each moment of collapse. It is observed that the isothermal model relying on the inertial bubble growth rate only, does not capture this transition of dominance and eventually fails to predict multiple pulsations. The physics-based cavitation model successfully captures the bubble pulsation beyond the second collapse. These findings highlight that explicit consideration of the non-isothermal effect is essential for problems with varying phase change regimes, and a phase change model reflecting this effect is vital for accurate computations. •High-fidelity multi-phase computational framework ACTFlow_MP is proposed.•New fluid property model based on artificial neural network is constructed for water.•The thermal effect turns out to be dominant at the moment of each bubble collapse.•The physics-based model successfully captured bubble pulsations beyond 2nd period.