<p>Accurate estimation of cutting tool reliability is crucial for optimizing tool replacement strategies, minimizing costs, and enhancing sustainability in machining operations. However, conventional reliability analyses often require multiple experimental replications, an approach that is costly, time-consuming, and impractical in industrial settings. This paper presents a novel simulation-based methodology that enables the estimation of cutting tool reliability using only a single experimental dataset, while preserving statistical rigor. The method models the expected tool life using Response Surface Methodology, while Poisson regression is applied to the squared residuals to estimate the variance. Subsequently, Weibull distribution parameters are estimated through constrained nonlinear optimization. To incorporate model uncertainty, Monte Carlo simulations are applied using the confidence intervals of the estimated mean and variance, generating a family of survival curves that represent statistically consistent reliability scenarios. Moreover, the method enables reliability estimation for any combination of cutting parameters within the experimental design space, substantially extending the utility of a single test matrix without the need for additional experiments. Experimental validation in a hard turning operation of AISI 52100 hardened steel demonstrated high accuracy and predictive robustness. By integrating statistical modeling and uncertainty analysis, the proposed method empowers manufacturers with a reliable, cost-effective framework for smart tool replacement decisions.</p>