Smart homes, an important application of the Internet of Things, use the Internet to monitor and analyze data gathered from appliances within the home automation system. These intelligent appliances enable users to oversee and manage the household’s energy consumption. The smart home system collects data on the appliances’ energy consumption, generating time series data. Using this data and a time series approach, we employ the Autoregressive Integrated Moving Average (ARIMA) model to analyze and forecast energy consumption based on data from international datasets. Specifically, we selected datasets representing energy consumption per capita in the European Union (EU-27) and Romania, chosen for its integration within the EU-27, allowing for both global and local insights. The forecast generated by the ARIMA model aims to evaluate the energy required, optimizing smart home energy consumption. Accurate forecasting is critical for developing efficient energy management systems in smart homes, enabling dynamic adjustments to household energy use and significantly reducing overall energy consumption. This research underscores the importance of precise energy consumption predictions to enhance the sustainability and efficiency of smart home energy management.