Mangrove ecosystems are an essential component of tropical and subtropical urban coastal regions where they provide critical ecosystem services and ensures climate mitigation while playing a pivotal role in the livelihoods of coastal communities. However, growing anthropogenic pressures from rampant urbanization and infrastructural demands are leading to an unparalleled loss and degradation of mangrove cover especially in coastal cities of the global south. Addressing the immediate need for monitoring, protection and restoration of the ecologically stressed mangroves, this study uses earth observations, machine learning and cloud computing methods for advancing timely and accurate spatiotemporal mangrove mapping and change detection. Image classification through four different models i.e. Classification and Regression Trees (CART), Random Forest (RF), Gradient Tree Boost (GTB) and Support Vector Machine (SVM) was performed using Google Earth Engine to classify mangrove extent along the coastal regions of Mumbai, India. Spatially explicit temporal trend in mangrove extent was studied and used to estimate the rate of change of mangrove extent over a period of 30 years. Accuracy assessment was conducted to validate the robustness of trained classifier models alongside their comparative performance. Classification accuracies on the order of 95% were achieved through the machine learning-based classifier models in distinguishing mangrove areas from other land cover types. The time-series analysis combined with image classification reveals the pattern and causes of spatiotemporal changes in mangrove cover and highlights the hotspots of mangrove loss and gain. This approach can aid stakeholders in the management and restoration of mangrove ecosystems through periodic and cost-effective monitoring of mangrove cover particularly in data deficient coastal cities. The outcomes of the study will contribute towards efficient decision-making in achieving the localization of Sustainable Development Goals 6 and 11 of the United Nations.