The speed of economic globalization will continue to accelerate in the future, but productivity growth is slowing and may even be significantly decoupled from expanding global supply chains. This chapter looks at how machine learning (ML) can be used to cut back on food waste and allocate labor efficiently with peak hour scheduling, demand forecasting, and revenue prediction in a way that improves the bottom line of restaurants. In this chapter, we suggest several methodologies with the help of different ML methods to forecast future labor demand and further enhance restaurant operations. We want to provide an affordable end-to-end solution for restaurant management in a wide range of scenarios, which will be aided by external data from these restaurants such as inside sales, outside covers, and temperature paired with point of sale (POS) data. This work is based on random forest regression, K-nearest neighbors, XGBoost, and support vector machine aiming to continuously enhance the sustainment of service design as well for efficiency direction in globalized economy.