In the rapidly changing world of online commerce, staying ahead and retaining customers has become a major challenge. This work is focused on creating and implementing a sophisticated system that can predict when customers are likely to stop using a service; this is achieved by using advanced techniques from machine learning and analyzing a wide range of customer data, such as their past purchases, browsing habits, and basic demographic information. Single-Layer Perceptron (SLP) though having an ample accuracy score of 92% falls short in handling complex data patterns and nonlinear tasks, making the proposed Multilayer Perceptron (MLP) a superior choice for overcoming the challenges. The proposed churn prediction system holds the promise of significantly boosting customer retention efforts; this has been projected with the Telco customer churn dataset, representing a fictional Telco company serving 7043 customers in California during Q3, where the MLP model achieved an accuracy score of 93%. The proposed system helps in fine-tuning marketing tactics, optimizing customer experiences, and ultimately paving the way for sustainable growth in an industry where competition is fierce and customer loyalty is paramount.