<p>High-speed milling (HSM) is advantageous for machining high-quality complex-structure surface components with various materials. Identifying and estimating cutting force signals for characterizing HSM is of high significance. However, considering the tool runout and size effects, many proposed models focus on the material and mechanical characteristics. This study presents a novel approach for predicting micromilling cutting forces using a semi-analytical multidimensional model that integrates experimental empirical data and a mechanical theoretical force model. A novel analytical optimization approach is provided to identify the cutting forces, classify the cutting states, and determine the tool runout using an adaptive algorithm that simplifies modeling and calculation. The instantaneous un-deformed chip thickness (IUCT) is determined from the trochoidal trajectories of each tool flute and optimized using the bisection method. Herein, the computational efficiency is improved, and the errors are clarified. The tool runout parameters are identified from the processed displacement signals and determined from the preprocessed vibration signals using an adaptive signal processing method. It is reliable and stable for determining tool runout and is an effective foundation for the force model. This approach is verified using HSM tests. Herein, the determination coefficients are stable above 0.9. It is convenient and efficient for achieving the key intermediate parameters (IUCT and tool runout), which can be generalized to various machining conditions and operations.</p>