Liquid biopsy has emerged as a transformative approach in modern diagnostics, offering a non-invasive means to detect and monitor cancer biomarkers such as microRNAs, circulating tumor DNA, and exosomes. Its versatility and potential for early detection have positioned it as a key player in advancing personalized medicine and real-time patient monitoring. However, challenges such as low biomarker concentrations and the need for accurate multiplexing persist. Lateral flow assays (LFAs) have evolved as versatile diagnostic tools, widely applied across diverse scientific disciplines. Recent advancements in artificial intelligence (AI) and automated image analysis have significantly enhanced the performance of LFAs, transforming them into user-friendly, point-of-care (POC) diagnostic devices. The integration of machine vision with LFAs represents a significant leap forward, enabling precise and real-time interpretation of results. This study introduces a novel multicolor LFA platform that leverages AI-driven image processing for the simultaneous detection and differentiation of three microRNA biomarkers (miR-21, miR-let-7a, and miR-155) in liquid biopsy applications. By employing distinct polystyrene beads as reporters, each color-coded to a specific microRNA, the system achieved multiplexed detection with limits as low as 1.56 fmol for each target. The innovative platform is paired with a smartphone-based application and a web application, which automate the reading and interpretation of test results, ensuring high accessibility and accuracy. The developed method was rigorously validated using real urine samples, demonstrating exceptional diagnostic performance with 99.3% accuracy, 99.1% sensitivity, and 100% specificity.