Recently, the increasing demand for cost reduction and productivity improvement in aerospace and parts manufacturing industries has driven the adoption of machining simulations. Simulations are valuable tools for predicting machining parameters, optimizing processes, and reducing costs by minimizing prototype production and test machining. However, discrepancies between simulation results and real-world machining outcomes remain a challenge due to tool wear, thermal deformation, and vibration effects. Thus, this study proposes a method to enhance machining simulation accuracy by incorporating actual machining load measurements. Initial simulations are conducted using various cutting depths, widths, and feed rates for simple machining paths. Machining loads are indirectly measured through real machining experiments. A correlation analysis between simulated and measured loads is performed, and predictive equations are derived based on the results. These equations are then applied to predict machining loads under different cutting conditions. The accuracy of these predictions is verified by comparing them with actual machining results. The proposed method effectively reduces the error between simulated and real machining outcomes, making simulation results more applicable to industrial settings. The average error between the predicted cutting load and the actual machining load was 4.7%. This study demonstrates that predicting machining load via simulations can improve accuracy, reduce trial-and-error efforts, and promote more efficient manufacturing practices.