Energy efficiency has aroused great interest in research worldwide, because energy consumptionhasincreasedinrecentyears,especiallyintheresidentialsector. Theadvancesinenergy conversion, along with new forms of communication, and information technologies have paved the wayforwhatisnowknownassmarthomes. TheInternetofThings(IoT)istheconvergenceofvarious heterogeneous technologies from different application domains that are used to interconnect things through the Internet, thus allowing for the detection, monitoring, and remote control of multiple devices. Home automation systems (HAS) combined with IoT, big data technologies, and machine learning are alternatives that promise to contribute to greater energy efficiency. This work presents HEMS-IoT, a big data and machine learning-based smart home energy management system for home comfort, safety, and energy saving. We used the J48 machine learning algorithm and Weka API to learn user behaviors and energy consumption patterns and classify houses with respect to energy consumption. Likewise, we relied on RuleML and Apache Mahout to generate energy-saving recommendations based on user preferences to preserve smart home comfort and safety. To validate our system, we present a case study where we monitor a smart home to ensure comfort and safety and reduce energy consumption.