The article reevaluates the Taylor Rule using two methods: linear regression and machine learning. The linear method suggests more accurate coefficients for the rule, while the machine learning method improves estimation accuracy by capturing nonlinear relationships between variables. The estimated federal funds rates closely match those actually implemented by the Federal Reserve Bank, except during three recessions caused by bubble bursts. The research provides theoretical insights and a more applicable model for predicting interest rates.