Viable Plate Count (VPC) measures the number of bacteria per gram (or milliliter) and is crucial for food safety, water quality, and clinical diagnostics, where accurate colony-forming units (CFU) counts guide decisions. Object detection offers a promising approach for counting and detecting colonies in VPC. By training models to accurately recognize and differentiate individual colonies, object detection systems can significantly enhance both the efficiency and precision of VPC. This research focuses on the development and evaluation of an object detection–based machine learning framework for automating VPC analysis. The aim is to reduce human workload, improve counting accuracy, and provide a scalable solution for high-throughput microbiological testing. Through model training, optimization, and validation, this work seeks to demonstrate a machine learning–driven VPC system. This system can serve as a reliable and cost-effective alternative to manual counting, contributing to faster and more consistent microbiological assessments. Although the accuracy needs to be improved, the AI is working perfectly.