This review article explores the transformative advancements in wearable biosignal sensors powered by machine learning, focusing on four notable biosignals: electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG), and photoplethysmogram (PPG). The integration of machine learning with these biosignals has led to remarkable breakthroughs in various medical monitoring and human–machine interface applications. For ECG, machine learning enables automated heartbeat classification and accurate disease detection, improving cardiac healthcare with early diagnosis and personalized interventions. EMG technology, combined with machine learning, facilitates real‐time prediction and classification of human motions, revolutionizing applications in sports medicine, rehabilitation, prosthetics, and virtual reality interfaces. EEG analysis powered by machine learning goes beyond traditional clinical applications, enabling brain activity understanding in psychology, neurology, and human–computer interaction, and holds promise in brain–computer interfaces. PPG, augmented with machine learning, has shown exceptional progress in diagnosing and monitoring cardiovascular and respiratory disorders, offering non‐invasive and accurate healthcare solutions. These integrated technologies, powered by machine learning, open new avenues for medical monitoring and human–machine interaction, shaping the future of healthcare. The convergence of wearable biosignal sensors and machine learning paves the way for significant advancements in healthcare, enabling early medical diagnosis and personalized health monitoring. This review article provides an overview of recent transformative advancements in wearable biosignal sensors powered by machine learning, focusing on four notable biosignals: electrocardiogram, electromyogram, electroencephalogram, and photoplethysmogram.