The growing complexity of urban environments in emerging economies highlights the urgent need for more resilient and sustainable municipal solid waste (MSW) management systems. Historically, reactive, labor-intensive, and resource-consumptive MSW management approaches may no longer prove to be adequate in light of increasing waste generation from urbanization, population growth, and limits in infrastructure capacities. This review acknowledges predictive analytics as a proactive and operationally efficient solution to MSW management through data-driven decision-making. By relying on historical trends, real-time data from sensors and individuals' socio-economic circumstances, predictive models make it practical to make informed predictions regarding waste generation patterns, route optimization, early detection of overflow events, and identifying infrastructure planning measures. Technical considerations that can be classified as building blocks that should be taken into consideration for predictive models (e.g., feature engineering, model selection, and validation paradigms) are briefly addressed. Case studies from most cities clearly illustrate the potential benefits of employing predictive analytics to reduce operational costs and environmental impacts while further driving down services costs. Nevertheless, implementation issues like uncoordinated data systems, low levels of technical capacity, and a lack of political will persist in most aspects of planning and delivery initiatives in low- and middle-income countries. This review posits that building integrated digital ecosystems, capacity-building activities, and inclusive governance structures will lead to a more substantial uptake.