Background: Artificial intelligence (AI) and machine learning (ML) are increasingly applied in respiratory medicine for diagnosis, prognosis, and clinical decision support. Despite promising results, clinical adoption is limited due to methodological, validation, and integration challenges. We aimed to systematically review peer-reviewed studies on AI/ML applications in the diagnosis, screening, prediction, and decision support for chest diseases. Methods: This review followed PRISMA guidelines. Eligible studies, published between 2020 and 2024, included human participants with conditions such as pneumonia, asthma, lung cancer, or osteoporosis, where AI/ML algorithms were applied to imaging or clinical data. Searches were conducted in PubMed, Scopus, Web of Science, and IEEE Xplore. Two reviewers independently performed study selection, data extraction, and quality assessment. A narrative synthesis was undertaken due to heterogeneity in study populations, AI methods, and outcomes. Results: Nine studies involving 55,117 participants were included: five randomized controlled trials, three retrospective diagnostic or observational studies, and one secondary RCT analysis. AI/ML applications demonstrated high diagnostic accuracy, workflow efficiency, and predictive capabilities across diverse clinical contexts. Notable outcomes included: a Random Forest model achieving an AUC of 0.907 for Pneumocystis jirovecii pneumonia diagnosis; AI assisted CT interpretation reducing reporting times by 22.1%; logistic regression detecting severe asthma exacerbations with 90% sensitivity; and AI-enabled chest radiography triaging increasing osteoporosis detection rates more than tenfold. Radiomics-based nomograms predicted lung cancer invasiveness and therapeutic response with AUCs >0.80. AI-based CAD improved detection of actionable lung nodules and enhanced non-radiologists’ chest X-ray interpretation accuracy. Conclusion: AI/ML tools show significant potential to improve diagnostic accuracy, efficiency, and risk stratification in chest diseases. However, broader clinical integration requires multicenter validation, standardized methodologies, transparency in algorithms, and evidence linking AI use to improved patient outcomes.