Smart home systems are the integration of technology and services through the network for a better quality of life. Smart homes perform daily housework and activities more easily without user intervention or with remote control of the user. In this study, a machine learning-based smart home system has been developed. The aim of the study is to design a system that can continuously improve itself and learn instead of an ordinary smart home system that can be remotely controlled. The developed machine learning model predicts the routine activities of the users in the home and performs some operations for the user autonomously. The dataset used in the study consists of real data received from the sensors as a result of the daily use. Naive Bayes (NB) (i.e. Gaussian NB, Bernoulli NB, Multinomial NB, and Complement NB), ensemble (i.e. Random Forest, Gradient Tree Boosting and eXtreme Gradient Boosting), linear (i.e. Logistic Regression, Stochastic Gradient Descent, and Passive-Aggressive Classification), and other (i.e. Decision Tree, Support Vector Machine, K Nearest Neighbor, Gaussian Process Classifier (GPC), Multilayer Perceptron) machine learning-based algorithms were utilized. The performance of the proposed smart home system was evaluated using several performance metrics: The best results were obtained from the GPC algorithm (i.e. Precision: 0.97, Recall: 0.98, F1-score: 0.97, Accuracy: 0.97).