This scientific review analyzes the application of remote sensing, geographic information systems and artificial intelligence (AI) technologies to the detection of slums and generally to the monitoring of urban sprawl in certain Moroccan cities and municipalities, particularly those located in the province of Kenitra, part of the Rabat-Salé-Kenitra region in northwest Morocco. Based on more than four major studies conducted between the years 2014 and 2023, the article presents a chronological and technical evolution of the methodology for detecting slums and urban sprawl ranging from the visual interpretation of satellite images to advanced machine learning and deep learning frameworks. Focusing on the urban dynamics of these cities and processing satellite images from Google Earth Pro and QuickBird using methods based on machine learning using support vector machines (SVM), mutual selection of information features and convolutional neural networks (CNN) by transfer learning. This review aims to innovate technological approaches in the context of current urban planning and address the challenge for the implementation of future urban planning documents aimed at transforming these cities into smart cities.