We present a study of six open clusters (Berkeley 67, King 2, NGC 2420, NGC 2477, NGC 2682 and NGC 6940) using the Ultra Violet Imaging Telescope (UVIT) aboard ASTROSAT and Gaia EDR3. We present their Gaia EDR3 membership catalogues and UV photometric catalogues along with membership probability. We used a supervised machine learning algorithm along with a Gaussian mixture model and used combinations of astrometric, photometric and systematic parameters to train the algorithm. We classified the stars as members, candidates and field using two such combinations. This generic technique is robust, reproducible, versatile in various cluster environments and is applicable to other data-sets. We could detect 200-2500 additional members using this method with respect to Gaia DR2 studies. We estimated cluster properties such as mean space velocities, distances, number of members and core radii. We detected 3 to 700 member stars, which include blue stragglers, main-sequence and red giants in various UVIT images of six clusters. We created UV-Optical colour-magnitude diagrams to find that majority of the sources in NGC 2682 and a few in NGC 2420, NGC 2477 and NGC 6940 showed excess UV flux. NGC 2682 images have 10 white dwarf detections in far-UV. The massive cluster NGC 2477 has 92/576 members detected in the far-UV/near-UV, which will be useful to study the UV properties of stars in the extended turn-off and in various evolutionary stages from main-sequence to red clump. Future studies will carry out panchromatic analysis of noteworthy members detected in this study.