The application of machine learning principles in the photometric search of elusive astronomical objects has been a less-explored frontier of research. Here, we have used three methods, the neural network and two variants of k-nearest neighbour, to identify brown dwarf candidates using the photometric colours of known brown dwarfs. We initially check the efficiencies of these three classification techniques, both individually and collectively, on known objects. This is followed by their application to three regions in the sky, namely Hercules (2{deg}x2{deg}), Serpens (9{deg}x4{deg}), and Lyra (2{deg}x2{deg}). Testing these algorithms on sets of objects that include known brown dwarfs show a high level of completeness. This includes the Hercules and Serpens regions where brown dwarfs have been detected. We use these methods to search and identify brown dwarf candidates towards the Lyra region. We infer that the collective method of classification, also known as ensemble classifier, is highly efficient in the identification of brown dwarf candidates.

Cone search capability for table J/MNRAS/488/2263/table8 (Brown dwarf candidates identified by NeuN and ensemble classifier in Hercules)

Cone search capability for table J/MNRAS/488/2263/table9 (Brown dwarf candidates identified by NeuN and ensemble classifier in Lyra)

Cone search capability for table J/MNRAS/488/2263/table10 (Brown dwarf candidates identified by NeuN and ensemble classifier in Serpens)

Cone search capability for table J/MNRAS/488/2263/table11 (The Gaia associations and their properties of the brown dwarf candidates identified by NeuN and ensemble classifier in all three regions)