The integration of industrial Internet of things (IIoT) technology with predictive maintenance methodologies has revolutionized the way maintenance operations are managed in industrial settings. This paper presents a comprehensive overview of predictive maintenance in the context of IIoT, focusing on the application of machine learning techniques for efficient and proactive maintenance strategies. The key components of IIoT predictive maintenance systems, including data acquisition, preprocessing, feature engineering, model selection, and deployment, are discussed in detail. Various machine learning algorithms commonly employed for predictive maintenance, such as support vector machines, random forests, neural networks, and deep learning models, are reviewed along with their strengths and limitations in different industrial scenarios. The challenges related to data quality, scalability, interpretability, and cybersecurity are addressed, and potential solutions are proposed. Case studies highlighting successful implementations of IIoT predictive maintenance solutions across different industries are presented to demonstrate the tangible benefits, including improved equipment uptime, reduced maintenance costs, and enhanced operational efficiency.