The optimization of cutting parameters is critical for improving machining efficiency and extending tool life. Colding’s equation is one such tool life prediction equation that can be used to optimize the machining parameters. However, the equation is complex and is often challenging to solve to evaluate its mathematical constants. This study investigates three distinct approaches for calculating the five constants (‘K’, ‘H’, ‘M’, ‘N0’, and ‘L’) in Colding’s equation. These techniques include analytical equation calculation and different curve fitting approaches. The primary objective was to assess the accuracy and effectiveness of these methods in predicting cutting parameters. Two workpiece materials, K1045 and Mild Steel, were used with results indicating that the Python 3.13 programming approach outperformed the other methods, including MATLAB R2024b and analytical calculations, achieving error percentages of 9.08% for K1045 and 5.51% for Mild Steel compared to 12.3% and 35.3% for K1045 and 12.4% and 64.3% for Mild Steel, respectively. Furthermore, the constants ‘N0’, ‘M’, and ‘H’ displayed different values for the two materials, indicating their dependence on workpiece material properties. Moreover, it was evident that Mild Steel exhibited better machinability than K1045 at low MRR (40–80 cm3/min), with up to 55.8% longer tool life, but K1045 performed better at medium and high MRRs, suggesting different machinability behaviours under varying cutting conditions. Overall, the findings demonstrate that Colding’s model, when applied with the appropriate computational method, can accurately optimize cutting parameters for different materials, contributing to more efficient and cost-effective machining processes.