Quinoline derivatives have attracted a lot of interest because of their numerous uses in agrochemicals, materials science, and medicines. Modern techniques for synthesising quinoline derivatives have developed recently, providing more effective, long-lasting, and focused pathways. These techniques provide excellent yields under softer circumstances by using contemporary catalytic procedures such microwave-assisted synthesis, metal-catalyzed cyclizations, and MCRs. To reduce harmful consequences, transition metal catalysts such as palladium, copper, and gold have been used in combination with green chemistry principles. Moreover, improved atom economy and functional group tolerance have been made possible by the invention of organ catalysts and photocatalysis. The conventional trial-and-error method of quinoline synthesis is being greatly reduced by the use of computational methods and machine learning models to forecast reaction paths and optimise settings. With an emphasis on their benefits over traditional methods in terms of sustainability, yield, and scope, this study seeks to give an overview of these modern approaches while highlighting the obstacles that still need to be overcome in order to increase substrate variety and scalability. Future developments in the large-scale synthesis of quinoline derivatives for industrial purposes are made possible by the investigation of these techniques.