As an important global process, urbanization affects economic, social and ecological systems. Urbanization progress monitoring and detection are critical issues in sustainable development, urban planning, and policy formulation. The conventional approach depends on manual interpretation of Satellite imagery, it is a time-consuming and energy-consuming approach. Recently, deep learning techniques have proven effective for automating the urbanization detection process on the basis of remote sensing data. The deep learning-based methods, including Convolutional Neural Networks (CNN), Fully Convolutional Network (FCN), Generative Adversarial Networks (GAN), and the latest Vision Transformers (ViT), applied for detecting urbanization progress, are reviewed thoroughly in this paper. Moreover, we introduce a new approach that combines CNNs for spatial feature extraction and Long Short-Term Memory (LSTM) networks for time-series analysis to monitor urban expansion. By utilizing commonly accessible datasets such as Landsat and Sentinel-2, we validate our method, which shows remarkable precision and fastness in reproducibly recognizing urbanization phenomena and forecasting their consequences.