Identification and characterisation of (un)lensed kilonovae (KNe) can be instrumental in improving our understanding of various aspects of cosmology and astrophysics, such as - measuring the Hubble constant, understanding the physics of the binary neutron star (BNS) merger, and studying the abundances of heavy nuclei elements. However, detecting (un)lensed KNe poses unique challenges due to their rarity and low brightness. Upcoming telescopes, such as Rubin-LSST -- with its deep imaging capabilities and wide field-of-view -- will provide a unique opportunity to observe these rare and faint transient events. Rubin-LSST will generate a deluge of data, making it essential to develop fast and efficient methods for identifying genuine (un)lensed events while minimizing false positives. To address this, we realistically simulate both unlensed and lensed KNe and test various strategies for efficiently detecting these events in the simulated light curve and image data. I will present the results of our latest simulations and detection methods.