Predictive maintenance represents a paradigm shift in oil and gas industrial asset management, replacing reactive approaches with data-driven condition-based strategies enabled by advanced technologies. This systematic literature review provides a comprehensive analysis of predictive maintenance applications in the oil and gas sector, examining technological approaches, sensor applications, performance outcomes, implementation challenges, and emerging trends. The review surveyed five major academic databases (Scopus, IEEE Xplore, ScienceDirect, ProQuest Computing DB, and Scite), adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Included papers discussed predictive maintenance using artificial intelligence, machine learning, Internet of Things, or advanced analytics, published between 2022–2025. Data extraction focused on methodologies, technologies, performance metrics, and implementation challenges. Analysis of 50 studies identified three evolutionary waves of predictive maintenance implementation spanning 2010–2025. Current implementations predominantly utilize IoT sensor networks, edge-cloud computing paradigms, and deep learning models, particularly Long Short-Term Memory units. Reported benefits include annual savings of $100,000-$300,000 per facility and 10–25% reductions in equipment downtime. Significant obstacles remain, including data quality standardization, legacy system integration, and model interpretability. Predictive maintenance technologies demonstrate notable operational, financial, and safety advantages across the oil and gas value chain. Future developments will focus on standardized data frameworks for cross-domain collaboration, digital twin integration, and hybrid AI models with enhanced generalization capabilities.