The complete blood count (CBC) is one of the most requested tests by physicians. Mostly realized in conventional hematological analyzers, CBC tests are restricted to centralized laboratories, due to frequent maintenance, size of devices, and expensive costs that these analyzers require. On the other hand, most handheld CBC devices commercially available present high costs and are not liable to calibration or control procedures, which results in poor quality compared to standard hematology instruments. The Hilab system is a small-handed novel hematological platform that uses microscopy and chromatography techniques for blood cells and hematimetric parameters analysis. Combining artificial intelligence, machine learning, and deep learning techniques, provides the main parameters evaluated in the CBC test and four-part differential WBC. For clinical evaluation, accuracy, precision, method comparison, and flagging capabilities of the Hilab System were compared with the Sysmex XE-2100 (Sysmex, Japan) results. Over the entire measuring range, a strong correlation (r > 0.9) between both methodologies was obtained for most parameters evaluated. Also, high accuracy (> 0.85), and adequate precision values were observed. The anticoagulant influence and the sample source (venous and capillary) effect were also evaluated, and no significant differences were observed (p > 0.05). Thus, considering the need for blood count point-of-care tests, especially for quickly patient management, the study indicated that the Hilab system provides fast, accurate, low cost, and robust blood cell analysis for reliable clinical use.