Background: Respiratory syncytial virus (RSV) infection is a significant burden for children under 5 years of age. While RSV is a common cause of acute lower respiratory tract infection (ALRTI), routine confirmatory testing is not widely practiced. Identifying risk factors for RSV infection can help inform clinical decision-making and guide appropriate management in emergency departments. The objective of this study is to determine the risk factors associated with RSV infection in children hospitalized with ALRTI. Methods: A retrospective case-control study was conducted among pediatric patients under 5 years of age diagnosed with ALRTI at Naresuan University Hospital between January 2018 and December 2020. Data were extracted from electronic medical records. RSV diagnoses were confirmed using a rapid RSV antigen test. Potential risk factors—including prematurity, family history of atopic disease, past respiratory illness, history of ALRTI contact, and underlying disease—were analyzed using a multivariable logistic model in Stata version 17.0. Results: Among 3,579 children hospitalized with ALRTI, 459 (13%) were diagnosed with RSV infection. Peaks in RSV-ALRTI were observed in September, October, and November across 2018–2020. Significant risk factors identified through multivariable logistic regression analysis included the rainy season [odds ratio (OR) =15.51; 95% confidence interval (CI): 1.74–138.42; P=0.01], daycare attendance (OR =3.00; 95% CI: 1.27–7.10; P=0.01), lung crepitations (OR =2.60; 95% CI: 1.23–5.50; P=0.01), and oxygen supplementation (OR =2.38; 95% CI: 1.07–5.30; P=0.03). Conclusions: This study highlights the significant clinical burden of RSV in ALRTI, with RSV-ALRTI accounting for 13% of all ALRTI cases. Notably, RSV-ALRTI cases peaked during the rainy season, specifically in September, October, and November. The identified risk factors for RSV-ALRTI include the rainy season, daycare attendance, lung crepitations, and the need for oxygen supplementation. These findings underscore the importance of early identification and management of high-risk patients, particularly during peak seasonal periods. Clinically, these risk factors can help guide targeted prevention and treatment strategies, improving patient outcomes and optimizing resource allocation in healthcare settings.