In this paper machine learning tools are explored for evaluation of microwave absorption in perovskite based compounds. Pervoskites are already popular as high dielectric materials in microwave devices. Trying these materials for their microwave absorption capability will provide a fresh alternative to the conventional carbon based absorber materials. With their efficient dielectric properties, pervoskites also provide other advantages like physical strength, chemical stability, high temperature withstandability, ease of fabrication and low cost. We have used machine learning tools to model the responses of a given dataset of pervoskites which can then be used as a building block for further predictive models for perovskite based microwave absorbers. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.