Liquid biopsy offers a non-invasive approach for the early detection of cancer and metastasis. Biomarkers such as microRNAs, circulating tumor DNA, circulating cancer cells, and exosomes, found in body fluids like blood, saliva, and urine, hold great promise for liquid biopsy applications. MicroRNAs, due to their small size (18-25 nucleotides) and instability, are particularly challenging to analyze. Various techniques have been developed for miRNA analysis. Lateral flow assays (LFAs) are widely used diagnostic tools, with applications spanning diagnostics, medicine, analytical chemistry, biochemistry, and environmental and food sciences. Furthermore, artificial intelligence and image analysis tools have emerged in analytical methods and (bio)sensors, enhancing the accuracy of the results. In this work, we developed an innovative multicolor LFA to visually detect and discriminate three different microRNAs (miR-21, miR-let-7a, and miR-155). The method includes i) the isolation of miRNAs form urine samples, ii) amplification of miRNAs by reverse transcription–polymerase chain reaction (RT-PCR), and iii) detection of the amplified products by lateral flow assay. For this purpose, we utilized polystyrene beads of distinct colors as reporters to distinguish between these targets, achieving detection limits as low as 1.56 fmol for each miRNA in urine samples. To enhance automation and accuracy, we integrated image processing in the form of a web and smartphone application, enabling automated result interpretation. The developed system was successfully applied to real urine samples, marking significant progress in LFA diagnostics, demonstrating 99.3% accuracy, 99.1% sensitivity, and 100% specificity.