Digital Twin (DT) technology has been used as a transformative approach for data monitoring and industrial process optimization, especially in older machinery contexts where retrofitting sensor networks is more difficult. This systematic review analyzed 803 publications from 2015 to 2025. The main focus was on sensor placement methodologies for DT applications in industrial machinery. The comprehensive database searches were done using Scopus and Google Scholar databases. We identified and categorized sensor placement approaches into six primary methodologies, including optimization-based methods, machine learning approaches, model-based techniques, information theory methods, rule-based/heuristic approaches, and hybrid methods. Our analysis shows a significant trend in the direction of data-driven and AI-enhanced approaches. For old machinery applications, we found that hybrid approaches combining physics-based models with machine learning show superior performance in scenarios with limited historical data. This work provides practitioners with a comprehensive taxonomy of sensor placement strategies. It highlights a trade-offs between implementation complexity, data requirements, and performance for old industrial systems.