Lewy Body Disease (LBD) encompasses a spectrum of neurodegenerative disorders, including Parkinson’s Disease Dementia (PDD) and Dementia with Lewy Bodies (DLB), characterized by abnormal protein aggregates known as Lewy bodies. This survey reviews the existing approaches, advancements, and open issues in using artificial intelligence for LBD research. We begin by outlining relevant data types, including clinical records, digital biomarkers, and neuroimaging data. These datasets can be used for training the machine learning (ML) and deep learning (DL) algorithms that may enhance diagnostic accuracy, predict disease progression, and identify potential therapeutic targets. The survey then delves into various ML and DL methodologies applied in LBD research. Supervised learning techniques, such as decision trees and support vector machines, have shown promise in classifying LBD subtypes and predicting patient outcomes. Unsupervised learning methods, including clustering and principal component analysis, help identify novel patterns in complex datasets. Additionally, DL approaches, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are increasingly utilized for their capability to analyze high-dimensional neuroimaging and time-series data. Despite these technological advancements, the survey highlights several persistent challenges. Data heterogeneity, quality, explainability, and privacy concerns remain significant obstacles. Integrating multi-modal data sources and the potential use of transfer learning to improve model robustness are discussed as promising future directions. This survey aims to provide a comprehensive overview of the intersection between data science and LBD research, offering insights into how ML and DL can be exploited to advance our understanding and treatment of this complex group of diseases.