In Basrah city, South of Iraq, urban growth monitoring is investigated in this study by using remote sensing and geographic information systems approach. The research aims to explore patterns and trends of urban sprawl in the city through the past decades, also applying urban sprawl modeling for future land use/cover prediction in the city. To conduct this research, a combination method was implemented, which consisted of two steps, quantify analysis of remote sensing data and qualitative examination of GIS data. Urban sprawl modeling techniques were used to analyze the data and examine patterns, which also third step is forecasting of future urban sprawl trends. The current results show an extraordinary growth of cities in Basrah that could have an impact on the environment, society and economy. The implication of the results for urban policy and planning. The significance of the research for the development of urban growth monitoring by utilizing integrated remote sensing and GIS is obvious in the current results. The outcome of this study would help for practitioners and urban policy makers for the informed decision-making and eventually for the successful urban planning. The principle aim of the research work was to investigate Basrah city by using several indices such as NDVI, NDWI, NDSI, NDMI, NDBI, MNDWI, BSI, CI, UI, SI, LST, and CA-ANN. The research found out that little vegetation, larger way of water presence in the study area based on the base images, changes in the level of soil salinity, vegetation vigor, very high pace of urban growth, changes in water features, changes in soil types, changes in soil dampness, increasing tendency of urban growth, soil degradation,temperature differences, land use chances and so on. To assess the future projections, the CA-ANN algorithm was generated using MOLUSCE plug-in. LULC maps of different time periods (10 years) were produced from 2002 to 2012 to detect the LULC changes. The LULC maps derived were used to predict the LULC patterns for the years 2030 and 2040. The findings showed that the urban region had a growth of about 27.41% from 2002 and 2022.On the other hand, the vegetation decreased by 42.13 percent, the bare land by 2.3 percent, and the water areas by 6.51 percent throughout the time. The LULC data reported that its future expectation from 2020 to 2040 in the metropolitan area is the growth rate that is 23.15 percent. On the other hand, the vegetation is predicted to lose 28.05%. The barren land will decrease 1.8%; as well as water bodies, which will decrease by 12.31%. For Basrah, there will be a 55.46% urban area by 2040. Additionally, the significance of different indices in understanding Basrah city’s environmental circumstances was made clear. The normalized soil index is a vital component of soil salinity research because it is known that soil salt levels are relative to BWL and DWL; hence, understanding soil salinity can mitigate the agricultural production and the attention of urban planning in Basrah city. The normalized moisture index is significantly relied on monitoring; it ascertains the health of the plants based on its moisture content. On the other hand, to quicken the multispectral satellite images a machine learning technique -MOLUSCE- model develop Machine learning models are used, which can aid in the time-consuming process and can help to interpretation of the future map. To achieve a controlled urban expansion and design, it’s imperative for government sectors to safeguard ponderable abundance of ecological and agricultural land in city. This research suggested the government sectors mastermind an Effective Sustainable Planning toward 2030 and explain the progress of accomplishing sustainable development goals, whose calamity, especially for poor nations, has been seriously increasing. Land Use and Land Cover (LULC) changing is authenticated that it can be modeled and predicted by Artificial Neural Networks (ANN). They have the considerable scope to handle complicated scenarios effectively through simulation because of its ability to recognize behaviors from large datasets. ANN used Multilayer Perception (MLP) is a widely adopted technique to handling LULC’s earlier transition and modifying while planning. This estimation of Land Use future projection has given the overall estimated accuracy 82.4% confirms the-validate of this simulation’s outcome.
