Currently, the lack of physical activity can lead to health problems, with the increase in obesity in children between 8 and 18 years old being of particular interest because it is a formative stage. One of the aspects of trying to solve this problem is the need for a standardized, less subjective, and more efficient method of evaluating physical condition in these children compared to traditional approaches. Objective: Develop a multiplatform based on computer vision technology that allows the evaluation of the physical fitness of schoolchildren using smartphones. Methodology: A descriptive cross-sectional study was carried out on schoolchildren aged 8 to 18 years of both sexes. The sample was 228 schoolchildren (128 boys and 108 girls). Anthropometric measurements of weight, height, and waist circumference were evaluated. Body mass index (BMI) was calculated. Four physical tests were evaluated: flexibility (sit and reach), horizontal jump (explosive strength), biceps curl (right arm strength resistance), and sit-ups (abdominal muscle resistance). With the information collected traditionally and by filming the physical tests, a computer vision system was developed to evaluate physical fitness in schoolchildren. Results: The implemented system obtained an acceptable level of precision, reaching 94% precision in field evaluations and a percentage greater than 95% in laboratory evaluations for testing. The developed mobile application also obtained a high accuracy percentage, greater than 95% in two tests and close to 85% in the remaining two. Finally, the Systematic Software Quality Model was used to determine user satisfaction with the presented prototype. Regarding usability, a satisfaction level of 97% and a reliability level of 100% was obtained. Conclusion: Compared to traditional evaluation and computer vision, the proposal was satisfactorily validated. These results were obtained using the Expanded Systematic Software Quality Model, which reached an “advanced” quality level, satisfying functionality, usability, and reliability characteristics. This advance demonstrates that the integration of computer vision is feasible, highly effective in the educational context, and applicable in the evaluations of physical education classes.