This review explores the applications of shallow learning techniques in smart city ecosystems, focusing on their practicality and computational efficiency in urban data processing. The paper examines how shallow learning models contribute to urban infrastructure management, predictive maintenance, resource optimization, service delivery enhancement, and urban system modeling. It highlights the advantages of these models, including cost-effectiveness, scalability, and suitability for structured datasets typical in urban systems. The review also discusses challenges, such as limited adaptability to complex tasks and scalability constraints, and suggests potential solutions like hybrid shallow-deep learning models and automated feature engineering. This study demonstrates its potential for creating reliable, efficient, and sustainable smart city solutions by addressing these limitations and leveraging shallow learning's strengths. The findings aim to guide researchers and practitioners in advancing the application of shallow learning in smart cities and inspire future exploration to meet evolving urban needs.