<p>Previous biosignal peak detection algorithms are limited by their lack of versatility, being highly specialized for specific signals, and their high computational complexity, which hinders their application in real time wearable devices. To overcome these problems, we propose a lightweight, general purpose ‘Look Around Searching Algorithm’ based on the Maximum a Posteriori (MAP) estimation principle from Bayes’ theorem, which focuses on the universal geometric characteristics of peaks. Furthermore, to enhance the robustness of complex ECG signal analysis, we introduce the ‘Preceding Priority Peaks’ rule, reflecting the physiological refractory period of the heart. The performance of the proposed algorithm was quantitatively validated using the MIT-BIH Arrhythmia Database, a representative electrocardiogram database. Its versatility was also verified by the proposed algorithm for other biosignals, such as PPG and respiratory signals. The experimental results demonstrated high reliability detection performance, achieving an average Sensitivity of 98.04% and a positive predictive value of 98.67% across all 48 records of the MIT-BIH database. Compared with previous electrocardiogram peak detection studies, the proposed algorithm does not show a dramatic improvement in detection accuracy. However, we confirmed its versatility across various biosignals and its potential to enhance computational efficiency. Therefore, the proposed algorithm is highly anticipated for future applications in real-time peak detection within multi-sensor-based digital healthcare and wearable computing environments.</p>