## 1 Introduction
### Review Eligibility Criteria  
This section outlines the criteria used to include and exclude existing studies for this review, ensuring relevance and consistency in the analysis. Figure 3 shows that the number of articles within the 'last five years' under the 'computer science' subject was collected from three well-known databases, including Elsevier, IEEExplore, and MDPI. A relevance table scoring '1 to 5' was established to smooth the inclusion and exclusion criteria. Articles with scores \(\geq\) 3 were selected for the final review. Likewise, duplicate articles were removed during the final stage. The Excel sheet with a range of columns, i.e., data type, database, disease, pre-processing techniques, AI models, performance, the purpose of the study, remarks, article type, and year of publication, was maintained for each database in which the extracted information was stored during the full-text review of every article. Finally, the acquired details were utilized to analyze and draw figures. Table 1 demonstrates the process for allocating relevancy scores to each article. The study included articles that provided detailed technical insights into computer science methods and algorithms while specifically addressing asthma. The selection process strictly followed the relevancy score criteria outlined in Table 1. The relevancy table was designed in accordance with our review methodology and research focus. If a reviewed article focused on pediatric asthma analyzed using ML or DL models, it was assigned a score of 2. If the study also incorporated environmental or respiratory data, the score increased to 3. Similarly, an article analyzing adult asthma along with environmental factors and EHR data using ML or DL algorithms received a score of 4, and so on. Since the authors of this review have expertise in both asthma and ML/DL methodologies, we carefully extracted relevant terms and maintained a checklist in an Excel sheet to systematically align with the scoring table, ensuring an efficient article screening process. However, one limitation exists: if a study, for example, focused on pediatric asthma while also incorporating environmental and EHR data analyzed through ML or DL methods, we determined the most appropriate score based on keyword relevance. In such cases, a score of 5 was considered, as the keywords strongly aligned with environmental and EHR data analyzed via ML/DL methods (relevant), while pediatric asthma (less relevant) was also included. Depending on the study's quality and alignment with our criteria, specifically, asthma, environment, and EHR data with ML/DL techniques, a score of 4 or 4.5 was assigned. Emphasis was placed on incorporating recent publications for a comprehensive review. Particular attention was given to studies that discussed pre-processing techniques and data sources. Additionally, research highlighting ChatGPT's role in asthma diagnosis, treatment, and management was specifically sought and included. Duplicate articles were removed, and ultimately, (n = 45) of the most relevant studies were selected. Papers with a relevancy score (<3) were excluded, as they lacked essential details on either asthma or AI modeling. This initial screening was conducted by reviewing abstracts to ensure the selected studies focused on both asthma and AI modeling. The key terms used in preparing search queries for database interactions are illustrated in Figure 4. Some of the research questions (provided in Section 7) were formulated to guide the investigation, focusing on the effectiveness, challenges, and advancements in ML, DL, and hybrid models. The remainder of this article is structured as follows: Section 2 provides an overview of the historical events of the AI models in analyzing asthma and environment data. Section 3 describes the data types and benchmark databases in asthma prediction. In Section 4, pre-processing techniques and a description of ML/DL models along with their performance are discussed. Section 5 demonstrates the effectiveness of ChatGPT's response for asthma management.