Neuromuscular disorders present significant challenges in diagnosis and treatment due to their complex nature. This chapter provides the application of artificial intelligence (AI) techniques for neuromuscular disorder detection using electromyography (EMG) signals. AI techniques have been employed to analyze EMG data and identify abnormal patterns associated with neuromuscular disorders. These techniques extract features from EMG signals, train models, and provide accurate classification and detection of disorders such as myopathy, amyotrophic lateral sclerosis, and neuropathy. Further research is needed to refine AI techniques, validate their performance, and integrate them into clinical practice for improved neuromuscular disorder detection and management. In this chapter, we will demonstrate the application of AI techniques in solving real-world healthcare problems, specifically focusing on neuromuscular disorder detection. The diagnostic process involves extracting relevant features from biomedical data and comparing them with known diseases to identify any deviations from normal data characteristics. An effective monitoring system should be capable of detecting abnormalities characterized by variations in the data. AI techniques offer the ability to automate biomedical signal analysis and classify patterns as normal or pathological by generating decision surfaces. Automatic detection and classification of biomedical signals using various signal processing methods have become critical for clinical monitoring. The objective of this chapter is to demonstrate the design of an efficient Python ecosystem for real-time monitoring, alerting clinicians when life-threatening conditions arise. At the end of each section, we will provide illustrative examples of suitable Python functions.