Ferroelectric Field-Effect Transistors (FeFETs) are a promising technology with immense potential for in-memory computing and AI acceleration. However, modeling their reliability remains a significant challenge due to multiple sources of variability. Design-time variability from process variations, run-time fluctuations driven by temperature effects, and the inherent stochasticity of ferroelectric domain switching—rooted in its probabilistic nature—make accurate reliability prediction highly complex. Without robust reliability models, it is impossible to ensure the accuracy and dependability of FeFET-based AI accelerator systems, which directly impacts the precision and effectiveness of AI algorithms. This talk presents a holistic framework for reliability estimation, seamlessly integrating insights from device physics to circuit-level analysis. We also highlight the transformative role of deep learning in addressing these challenges, demonstrating how it enables precise reliability modeling and unlocks the full potential of FeFET technology for next-generation computing.