Recently, one of the environmental topics that has been studied and discussed the most among the researchers is global warming and climate change. Globally, there has been a lot of focus on the variability of the climate, especially about the rainfall and temperature fluctuations. Therefore, it is crucial to analyse climate-induced changes and recommend optimal adaptation policies, particularly in those nations where agriculture primarily depends on rainfall. This study investigates the long-term rainfall pattern and its effects on achieving the “Sustainable Development Goals (SDGs)” in Birbhum district, West Bengal, India, part of the red and lateritic agroclimatic zone over the sub-tropical environment. Trend detection methods like “innovative trend analysis (ITA)”, “Mann Kendall (MK)”, “Modified Mann Kendall” and “Bootstrapped Mann Kendall (BMK)” were used to assess the problems. The rainfall prediction up to 2037 was evaluated using the “multi-layer perception-artificial neural network” model. The result of this study indicates a declining rainfall pattern during post-monsoon periods and an increasing annual rainfall trend observed in winter, pre-monsoon, as well as in monsoon periods. The prediction result of this study also shows a growing rainfall trend. The most significant key objective of this study is the impact of increasing rainfall on the environment and society, considering optimal pathways to achieve SDGs. This study directly connected SDGs 2, 6, 13, and 15 goals and respective targets under climate variability and increasing rainfall trends.