Ensuring water quality is essential to safeguarding public health, as contaminated water is a leading cause of diseases such as cholera, dysentery, hepatitis, and typhoid fever. Conventional methods for monitoring bacterial contamination include membrane filtration, multiple tube fermentation, and enzyme-based assays. These methods are highly reliable but are constrained by lengthy processing times and require expensive equipment, consumables, and trained personnel. This work presents a portable UV-LED/RGB sensor system designed to address these limitations by using a multi-well self-loading microfluidic device for sample preparation-free analysis, a defined substrate assay specific to Enterococcus faecalis, a thermoelectric heater for assay incubation, UV-LEDs for sample excitation, RGB sensors for emission acquisition, a 3D-printed casing, and a microcontroller, achieving fast, low-cost, portable, and automated bacteria quantification. Wells in the microfluidic device are independent from each other and are designed to autonomously load with sample water when the device is submerged. The number of wells and volume per well are designed for bacterial quantification using Most Probable Number (MPN) analysis. Fluorogenic assay reagents are pre-loaded into each well of the microfluidic device and dissolve when the wells are loaded with sample water. Fluorescence signals captured by the RGB sensors are analyzed using machine learning (ML) algorithms including a Multilayer Perceptron Neural Network (MLPNN), which determines whether individual wells will be positive or negative by the end of a 24-hour period. The results show 100% accuracy in classifying bacterial presence within wells and a remarkably low detection time of under 30 minutes. The novel combination of ML and MPN analysis in an automated and cost-effective manner allows for near-real-time bacterial quantification and marks a significant advance in rapid bacteriological water quality analysis. The innovations presented offer a robust solution for on-site water quality monitoring, advancing public health and enabling faster responses to potential waterborne contamination.