Land surface temperature (LST) varies inside and around a city because of differences in surface cover, thermal capacity, and three-dimensional geometry. To better understand how to measure urban heat islands (UHI) and urban cool islands (UCI) using multispectral remote sensing data (MSRS), this study aims to summarize the current status of the field. By analyzing previous research that uses software (e.g., ERDAS Imagine, ArcMap, QGIS) to estimate UHI/UCI, this study aims to identify challenges and unanswered questions in this field. A systematic review was conducted following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) standards. Using a combination of keywords, titles, and abstracts, we systematically gathered and evaluated research papers from different databases. The UHI mapping of humid and vegetated (temperate) zones was found to have made great progress. However, few studies have looked at how changes in land use/land cover (LU/LC) affect LST in arid and semi-arid regions. There has been some development in replicating UHI and UCI, but progress has been gradual. More research is needed on UHI/UCI in dry and semi-dry regions. The current review can assist researchers in understanding existing approaches to UHI or UCI measurement using MSRS data and contribute to shaping future studies.Graphical AbstractThe graphical abstract provides a concise and visually engaging summary of the present study, which offers a systematic review of the urban thermal climate with an emphasis on urban heat islands (UHI) and urban cool islands (UCI), assessed through multispectral remote sensing (MSRS) techniques. The illustration highlights key satellite datasets (MODIS, Landsat, and Sentinel), which are critical for retrieving land surface temperature (LST) and monitoring urban thermal dynamics over time. The processing of satellite data is supported by widely used geospatial tools such as ERDAS Imagine, ArcGIS, ArcGIS Pro, QGIS, and Google Earth Engine (GEE), which facilitate spatial mapping, classification, and temperature estimation workflows. The methodology follows the PRISMA (preferred reporting items for systematic reviews and meta-analyses) framework, systematically identifying, screening and selecting relevant studies based on specific inclusion criteria relevance to UHI or UCI phenomena. The review found that UHI research is well developed in humid and temperate regions, with extensive mapping and analysis available. However, studies focusing on arid and semi-arid climates are limited, revealing a significant research gap. Importantly, the findings emphasize that urban geometry, surface materials, and landscape configuration have substantial impacts on UHI intensity. Future studies should integrated artificial intelligence (AI), machine learning (ML), and deep learning (DL) models to improve accuracy in UHI and UCI prediction and spatial modeling.