This study reports the designing of BChE inhibitors through machine learning (ML), followed by and evaluations. ML technique was used to predict the virtual hit, and its derivatives were synthesized and characterized. The compounds were evaluated by using various tests and methods. The gradient boosting classifier predicted as an active BChE inhibitor. The derivatives of the inhibitor, i.e., compounds , and were potent BChE inhibitors and displayed blood-brain barrier permeability with no significant AChE inhibition. The ML prediction was effective, and the synthesized compounds showed the BChE inhibitory activity, which was also supported by the studies.