Parkinson's disease (PD) is a neurodegenerative disorder affecting millions worldwide, characterized by motor symptoms such as tremor, rigidity, and bradykinesia that significantly impair daily life. The current diagnosis and monitoring rely primarily on clinical observations and rating scales (e.g., the MDS-UPDRS), which are subjective and limited in detecting subtle motor alterations, leading to inter- and intra-rater variability. In recent years, wearable sensors such as surface electromyography (sEMG) and inertial measurement units (IMUs) have emerged as non-invasive tools for quantifying neuromuscular activity and motor performance in PD. When combined with machine learning (ML) and deep learning (DL) techniques, these signals enable the development of models for disease detection, patient classification, and symptom severity assessment. This review provides a structured overview of recent ML and DL approaches applied to surface electromyography for PD monitoring, addressing a gap in the current literature. It analyzes data acquisition strategies, preprocessing techniques, feature extraction methods, model architectures, and evaluation protocols across tasks such as diagnosis, tremor analysis, freezing of gait detection, and gait assessment. Despite promising results, key challenges remain, including limited dataset size, lack of standardization, and poor generalization. Finally, this work highlights emerging trends and identifies a representative processing pipeline to support real-world clinical translation.