This research investigates household inflation expectations survey data from the Reserve Bank of India using a machine learning approach. The objective of this work is to extract insights from the survey data using both supervised and unsupervised machine learning approaches. A 15-month period, from January 2022 to May 2024, was used to gather the secondary data. Two clustering algorithms—fuzzy c-means and k-means clustering were applied to find distinct clusters of households according to their inflation expectations. However, the poor cluster quality based on silhouette coefficient indicates that households’ expectations vary widely and are influenced by complex factors. Followed by clustering, this study attempted to apply classification algorithms such as Decision tree, Random Forest, Gradient Boosted, and Multilayer perceptron (MLP) neural network keeping view on inflation expectation in general after 1 year as the target variable which had 5 classes. Among the four models, Random Forest outperformed the rest of the models with 90% accuracy and the important features identified. The findings from this study would improve our understanding of which demo-graphic or socioeconomic groups are more likely to form expectations in a particular way, which will help the RBI tailor its communication strategies. The current study adds to the body of knowledge regarding inflation expectations, specifically with regard to India. .