This study, investigates the goal setting and goal optimization using machine learning techniques. Goal setting assesses whether a goal is achievable; if so, it helps define the main goals, sub-goals, and establish a plan. During the study, we analyzed a three-year sales dataset and predicted prices that would achieve a 20% revenue increase goal for the year following the last day of the dataset. We implemented the time series forecasting models for this study and applied the goal optimization methods. We tested six different time series models, and based on accuracy values, we benchmarked the Seasonal Autoregressive Integrated Moving Average (SARIMAX) model with the highest success rate. Goal optimization is implemented using the Python programming language with time series and optimization libraries. While departments within companies typically spend days working on pricing issues to reach the target revenue, this study offers a rapid and smooth solution for the goal optimization. In addition to saving time for companies, it also helps save money and prevents excessive risk-taking beyond the target goal, ultimately enhancing customer satisfaction and ensuring the company’s sustainability. From a broader perspective, it contributes to supporting sustainable economic growth, thereby assisting in achieving long-term economic development.