The challenges posed by weather alteration have been widely acknowledged as significant obstacles to conservation efforts. Recent research has demonstrated the feasibility of identifying the consequences of a changing climate on biological systems. As environmental change is a global problem demanding urgent attention, numerous studies have been conducted to explore this topic and develop strategies for adaptation. However, addressing the intricacies of environmental change necessitates the development of novel approaches. In recent years, Deep Learning (DL) techniques have gained popularity in various industries, including environmental change. This study aims to investigate the most commonly utilized DL techniques for combating and adapting to environmental change. Moreover, it aims to classify the most extensively studied mitigation and adaptation measures, with a particular focus on urban regions, utilizing DL techniques. The results indicate that the most widely employed DL approach is also the most effective in mitigating and adapting to environmental changes. Additionally, DL algorithms have been extensively utilized in geo-engineering and land surface temperature studies within the field of environment change mitigation and reworking. This work analyzes the significant influences of local environmental conditions and climate on various weather characteristics, encompassing temperature, humidity, clouds, and wind speed. The study utilizes weather data from Haryana, an Indian state, spanning from January 1, 2012, to December 31, 2022. The findings reveal that the highest wind speeds in this region occur in the month of June, reaching approximately 9 km/h