The integration of machine vision techniques in biomedical signal and image processing represents a pivotal advancement in healthcare diagnostics and analysis. This study investigates the efficacy of various machine vision algorithms in enhancing the interpretation and analysis of biomedical data. Leveraging Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN) among other algorithms, our research aims to revolutionize diagnostic accuracy and anomaly detection in biomedical imaging and signal interpretation. Our findings demonstrate exceptional accuracy rates, with RF achieving 94.2% accuracy, outperforming other algorithms in lesion detection by exhibiting a 17% increase in detection rates. Additionally, CNN showcased a notable enhancement in signal-to-noise ratio, providing a 35% increase, while SVM exhibited a remarkable 23% reduction in false positives. The execution time analysis revealed that the Transformer-based Network exhibited the fastest processing time of 4.1 s, underscoring its efficiency in real-time analysis. These results signify the potential of machine vision algorithms in augmenting biomedical diagnostics, paving the way for precise disease identification, treatment monitoring, and personalized healthcare.