In recent years, climate change issues have attracted enormous attention from the scientific community and policymakers due to their direct impact on sustainable development. Various natural hazards such as typhoons, heat waves, extreme and uneven distribution of rainfall, floods, drought, forest fire, and others have shown a rapid rise globally and they are affecting the natural resources, economy, and infrastructure at the regional and global scale. The assessment of short and long-term consequences of climate change needs new, fast, and economical technologies. Current remote sensing technologies associated with machine learning techniques have shown their capabilities in this regard. The outcomes of remote sensing analysis are being used to monitor land, ocean, and atmosphere at a regional to a global scale with high temporal resolution. In the examination of various climate extremes, natural hazards, and eco-environmental challenges, remote sensing data has been introduced using conventional, and machine learning-based techniques. Recently, new indices Normalized Difference Latent Heat Index (NDLI) and Temperature-Soil Moisture Dryness Index (TMDI) are introduced for remote sensing analysis of surface soil moisture assessment. These indices have shown their edge over other conventional indices and can also be used in drought assessment analysis. Using remote sensing analysis, the eco-environmental vulnerability issues are addressed and the vulnerability maps to signify the changes in the eco-environment worldwide due to human-made and natural disturbances are produced. Natural hazards like Typhoons may cause major impacts on infrastructure and human life. The impact assessment of typhoons on the urban green space of Taiwan is carried out using remote sensing data from Copernicus Program. Furthermore, Sentinel 5P data is used for air quality analysis during typhoons and dust storms. The impact of climate change on typhoons' cyclogenesis, tracks, and intensity has been assessed as Taiwan suffers no landfall in recent years which leads to severe drought in 2021. Machine learning techniques have been introduced to investigate typhoons and shown helpful in the development of AI-based typhoon models. The extension program of major cities is altering the green cover and hence affecting air quality and human health. Hence, it is also necessary to access these changes in time to help mitigate the human health risks. In this regard, the urban greenspace patterns have been investigated using remote sensing datasets to highlight the impact of change in the urban green space over the heat mitigation process. The abovementioned technological achievements are useful in the management of various earth and environmental issues with low cost and better coverage. They will also help scientific community and policymakers in planning of sustainable society.