The rate at which the world is evolving is astonishing with cutting-edge technologies being introduced every day. There have been developments in every field ranging from constructing gigantic architectures to enhancing the ease and comfort with which we can commute daily. Nevertheless, this has given rise to different types of pollution, sound pollution being one of them. Although the impact of sound pollution is not instantaneously noticed, it has a distinct effect on humans and nature which is observed over a period of time. There are multiple sources that cause sound pollution. Sounds from ongoing construction sites are one of them. Such sounds disrupt the daily lifestyle of people, their health, and daily productivity. Mostly, these sounds start very early in the day thereby making it difficult for people to manually take precautions like shutting doors and windows or deploying mobile soundproofing solutions. Machine learning has advanced significantly over the years leading to the refinement of artificial intelligence. This has aided in the development of multifarious solutions to ease and simplify our daily lifestyle. This sets the stage for an autonomous system, that can detect sounds from construction sites very early to automatically deploy precautionary measures so that the effect of these sounds on close by residents can be minimized. In this paper, a system is presented that models audio clips using MFCC-based features for the identification of sounds from construction sites in the Urban environment. Experiments were performed on a dataset of over 6K clips composed of the varied construction sounds as well as ambient urban sounds and a maximum accuracy of \(98.02\%\) was achieved using random forest-based classification.