Respiratory sound analysis is becoming increasingly popular as a non-invasive method for detecting adventitious sounds and aiding in the diagnosis of respiratory diseases such as chronic obstructive pulmonary disease (COPD), asthma, and pneumonia. However, studies in this field differ significantly in datasets, recording devices, annotation techniques, preprocessing pipelines, model architectures, validation strategies, and evaluation metrics. This scoping review examines current research on artificial intelligence-based respiratory sound analysis, with a focus on datasets, acquisition and annotation practices, signal processing, feature representations, machine learning (ML) and deep learning (DL) methods, and evaluation protocols. The search identified 1056 database records, and 89 reports were included for the final evidence mapping. The reviewed datasets support event-, cycle-, recording-, and patient-level tasks and differ considerably in population, scale, acquisition hardware, annotation granularity, and label structure. The findings also show that acquisition and annotation are closely linked, creating potential device-, recording-site-, and label-related confounding. Methodologically, the literature can be summarized in four broad stages: handcrafted feature-based ML; deep spectrogram learning; representation learning and multimodality; and deployment-oriented, robustness-focused systems. Despite recent achievements, the field remains limited by small and imbalanced datasets, annotation uncertainty, device and population variability, inconsistent data splitting, and limited external validation. More reliable clinical use will require standardized acquisition and annotation, quality-controlled preprocessing, patient-independent evaluation, task-appropriate metrics, transparent reporting, and validation across independent devices, datasets, and clinical populations.