Transaction on Machine Design Dit Suthiwong 14 2 2026 https://doi.org/10.6025/tmd/2026/14/2/67-84 https://www.dline.info/tmd/fulltext/v14n2/tmdv14n2_2.pdf Accurately predicting cutting tool life is essential for minimizing unplanned downtime and optimizing predictive maintenance in modern milling operations. While existing tool condition monitoring studies focus primarily on wear detection, they often lack integrated degradation trajectory modeling and reliability assessment. This study proposes a comprehensive Prognostics and Health Management (PHM) framework combining vibration based condition monitoring with stochastic state space modeling to evaluate tool wear progression. Utilizing the "Roughness in Milling Process" dataset, which pairs tri-axial vibration signals with surface roughness measurements across eight workpieces, the research constructs a normalised degradation index to track tool deterioration. The methodology incorporates wear state classification, Hidden Markov Models (HMM), and Hidden Semi-Markov Models (HSMM) with Weibull duration distributions to capture realistic state transitions. Furthermore, an absorbing Markov chain framework is applied to reliability analysis, alongside Monte Carlo simulations for probabilistic estimation of Remaining Useful Life (RUL). Results demonstrate a strong monotonic correlation between increasing vibration magnitudes and worsening surface roughness. The HSMM effectively modeled prolonged intermediate degradation stages, while probabilistic RUL forecasts provided actionable insights for maintenance scheduling. Although constrained by the dataset's limited size, this integrated approach successfully bridges raw sensor data with actionable reliability metrics. Ultimately, the proposed framework establishes a robust, data-driven foundation for next-generation predictive maintenance strategies aligned with Industry 4.0 intelligent manufacturing paradigms.