The rapid advancement of industrial automation, wireless sensor technologies, cyber-physical systems, andintelligentmanufacturing has transformed maintenance strategies from reactive and preventive approaches toward predictivemaintenance.Predictive maintenance (PdM) utilizes historical equipment data, sensor measurements, and machine learning algorithmstoidentifypotential equipment failures before they occur, thereby minimizing unplanned downtime, reducing maintenance costs, andimprovingoperational efficiency. During the period between 2008 and 2015, significant research efforts focused on condition-basedmaintenance,machinery prognostics, fault diagnosis, sensor data analytics, and intelligent decision-support systems, establishing thetechnologicalfoundation for modern Industrial Internet of Things (IIoT)-based predictive maintenance systems. Although the widespreadadoptionofIndustrial IoT accelerated after 2015, the underlying methodologies were developed during this foundational period. This studypresentsan experimental machine learning approach for predictive maintenance in Industrial IoT environments. The proposedframeworkintegrates industrial sensor data acquisition, signal preprocessing, feature extraction, machine learning-based fault prediction,maintenance decision support, and performance evaluation into a unified predictive maintenance architecture. Theframeworkinvestigates widely used machine learning algorithms, including Decision Trees, Support Vector Machines (SVM), Artificial NeuralNetworks (ANN), Naïve Bayes, Random Forests, and Logistic Regression, for equipment health monitoring and failure prediction. Keywords: Predictive Maintenance, Industrial Internet of Things, Industrial IoT, Machine Learning, Condition-Based Maintenance.