Water is a necessary component of all human activities. According to the United Nations WorldrnWater Assessment Program, every day, 2 million tons of sewage, manufacturing, andrnagricultural waste are discharged into the world's water. Due to population demands andrndwindling clean water supplies as well as available water pollution management mechanisms,rnthere is an urgent need to use computational methods to intelligently manage available water. Tornensure the protection of drinking water, accurate detection of natural or deliberate pollutionrnevents in water delivery pipes is essential. Companies that have water must ensure that it is safernto drink. To resolve the global issue of rising water contamination, the design of waterrncontamination detector models has monitored the security of water in pipelines whenrnconcentrations of water quality variables in the pipes surpass their maximum threshold isrnpresented in this paper. This paper proposes artificial neural networks, specificallyrnConvolutional Neural Networks, for automated water impurity, detection to refine the modelrnmust a picture of turbid water in the pipe is used to detect events. The algorithm of deep learningrnachieved 96.3 percent accuracy after extensive training with a dataset of 4220 images reflectingrnvarious levels of contamination. Besides that, the machine learning algorithm uses an efficientrnstudy of water turbidity and transparency levels to estimate the level of pollution in a specificrnsample of water. As the established model is combined with the current framework, it willrnprovide a cost-effective way for the water company to obtain an estimate of water quality,rnalerting local and national governments to take action, and potentially saving millions of peoplernthroughout the world.