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Determining velocity accurately from real-world data is crucial in many scientific and technical fields. However, there are many obstacles to overcome when attempting to extract accurate velocity information from unclear or inconsistent datasets. In this research article, we provide a thorough explanation of the application of discrete least squares (DLS) software for velocity analysis using actual data analysis. DLS provides a strong framework for estimating velocity from continuous datasets by minimizing the sum of the differences between actual and predicted values, using the concepts of least squares optimization. By using theoretical explanation, realistic examples, and quantitative verifications utilizing a variety of datasets, we clarify the effectiveness and suitability of DLS for correctly and efficiently obtaining velocity data. In addition, we go into the mathematical foundations, computational techniques, and practical problems related to DLS implementation, offering useful knowledge. Additionally, we go into the conceptual foundations, practical issues, and computational techniques related to the implementation of DLS, offering insightful information to researchers as well as practitioners. This research is significant because it has the potential to improve velocity prediction from real-world data in terms of accuracy and dependability. This will help with decision-making and advance scientific understanding in a variety of fields. This work advances knowledge and innovation in domains ranging from biology and economics to physics and engineering by providing researchers with strong tools and methodologies for velocity analysis. Overall, our research demonstrates that discrete least squares is a flexible and effective method for obtaining useful velocity information from large, complicated datasets, providing new opportunities for investigation and learning across a variety of fields.