This paper presents a concise yet effective technique for simplifying complex nonlinear equations by converting them into a log-linear form, with particular application to Dynamic Stochastic General Equilibrium (DSGE) models. The method, known as log-linearization, leverages Taylor series approximation to express nonlinear functions as linear relationships in logarithmic deviations around a steady-state equilibrium. By doing so, the approach enhances analytical tractability and facilitates the interpretation and solution of DSGE models. The paper provides illustrative examples, including applications to Real Business Cycle (RBC) models, to demonstrate the practical implementation and benefits of the technique in macroeconomic modeling.