The convergence of signal processing techniques and artificial intelligence methods in biomedical signal analysis is leading to a paradigm shift in healthcare. This shift is characterized by precision medicine and sustainable healthcare practices. This integration utilizes artificial intelligence (AI) to enhance traditional processing methods, allowing for a more thorough and detailed analysis of biomedical signals, including electrocardiograms, electroencephalograms, and medical imaging data. The algorithms of machine learning and deep learning enable AI systems to detect subtle abnormalities in biomedical signals with unmatched accuracy and efficiency compared to conventional methods. This results in improved diagnostic accuracy, enabling healthcare providers to identify diseases at earlier stages. Furthermore, healthcare practitioners can use AI-powered decision support systems to create personalized treatment plans based on individual patient characteristics and medical histories. These systems analyse patient data in real time and synthesize evidence-based guidelines to optimize treatment outcomes while minimizing the risk of adverse effects. Additionally, AI-driven predictive analytics can forecast disease progression and identify patients at high risk of complications.