Linear regression analysis is one of the most common statistical methods used to investigate trends in rainfall over time. However, conventional regression models usually require datasets that have been completely and accurately determined for trend analysis with rainfall, the datasets often include monthly or annual averages which can result in uncertainty in trend analysis. In order to incorporate this uncertainty, fuzzy set theory represents the ambiguity from the analyzed datasets. The fuzzy regression models consider both input and output variables to be Triangular fuzzy numbers. This study proposes a fuzzy regression approach that describes the relationships between precipitation and time in a context of excessive variability in rainfall which creates challenges for the sustainable management of water resources. Kalahandi district in Odisha has been acknowledged as one of the impoverished districts in the KBK Region. The utilization of the fuzzy regression model in addressing trend variability or trends in rainfall in Kalahandi, Odisha is a comprehensive investigation of historical rainfall records related to trends and variability of rainfall in Kalahandi, especially the region’s severe effects associated with climate change; it is of utmost importance to understand how rainfall variability trends have changed over time for local agrariens or agriculture workers and policy-makers to recognize changes in rainfall patterns and develop adapted agriculture plans and manage water resources in a variable climate. The data collection process included 100 years of fuzzy rainfall data (1923-2023) which helps to frame current rainfall variability trends in Kalahandi. From this study, it has been shown that Kalahandi is experiencing climate change or other environmental factors that are leading to a substantial increase in rainfall variability. The study methodologically establishes fuzzy regression as an alternate approach to provide a more accurate predictive model for uncertain rainfall.