Accurate tool wear prediction in machine tool operations plays a vital role in achieving high machining quality while contributing to green manufacturing by reducing waste, lowering energy consumption, and enabling sustainable tool management. However, existing deep learning-based approaches have difficulty predicting the remaining useful life (RUL) of tools when future sensor data are unavailable. In this paper, we propose a novel framework that combines a physics-based polynomial wear model with a transformer model to enable reliable RUL prediction even in the absence of additional input data. First, an adaptive labeling technique is used to correct non-monotonic measurements commonly found in industrial environments, thereby enhancing the reliability of the training data. Next, a transformer model— optimized via 25 Bayesian searches over the number of layers (1–6) and feedforward dimensions (128–8192)—learns from historical sensor data to precisely estimate tool wear. Building on this estimation, a physics-informed model is applied to predict the wear trend without requiring future sensor data. Finally, a kernel density estimation (KDE)-based ensemble of 10 independently trained models probabilistically aggregates multiple outputs, reducing sensitivity to outliers and further improving prediction accuracy. Validation using NASA’s open-source CNC milling dataset comprising 16 machining cases demonstrates that the proposed framework achieves RMSEs of 0.098 for tool wear estimation and 1.119 for RUL prediction, outperforming existing methods. By integrating physics-based modeling into a deep learning framework, this paper shows that tools’ RUL can be accurately predicted without additional sensor data, contributing to more efficient tool replacement scheduling in real-world processes.