This paper has identified that property management needs help managing operating expenses as key expenses such as utilities, maintenance, and labor continue to rise. Predictive analytics addresses these challenges well, providing an empirical tool for property managers to get a head start concerning their undertakings. This paper outlines how this utilizes operational and historical data to manage energy use, determine equipment faults and maintenance requirements, and manage a workforce. With the help of moderate statistical models, machine learning, and data collected by IoT sensors, predictive analytics enables the discovery of inefficient solutions, the prediction of high-cost events, and the prevention of many unexpected repairs. This paper then presents a case study that shows how running residential prospects can be optimized through predictive analytics in areas such as energy consumption, tenant behavior, and maintenance. According to the results obtained here, it is possible to realize operational expense cuts of about 15% due to preventive maintenance measures, rational use of resources, and energy-saving measures. Through predictive models, tenant satisfaction is also improved because the likelihood of disruption and maintenance is reduced. Potential start-up problems include data quality, setup costs, and other problems, which are usually common when implementing new software at its initial use stage. This paper aims to analyze the strengths of predictive analytics as an enabler of sustainable cost management and efficient operations. Better developments of higher AI, IoT-enabled applications, and personalized programs are expected to enhance the predictive models for improving the property management processes.