The aim of this study was to determine the effect of air pollutant particles on the cardiovascu-lar disease burden (CVDALY) in Türkiye. Particulate Matter 2.5 (PM 2.5) and Non-methane volatile organic compounds (NMVOC) were taken as the independent variable and CVDALY as the dependent variable. The variables were analyzed within the Panel Data Analysis and Machine Learning Approaches frame. Unidirectional Granger causality was determined from PM 2.5-NMVOC to CVDALY and revealed that they acted together in the long term. The regression analysis that was made using econometric and multivariate regression models re-vealed that generally 1 unit increase in PM 2.5 increased CVDALY by between 0.0021–0.0029 units; 1 unit increase in NMVOC increased CVDALY by between 0.00024–0.0004 units. In Machine Learning approach, it had been determined that if the PM 2.5 and NMVOC were reduced to 0.84- and 9.48 respectively; CVDALY would be decreased to 0.022. In other words, Machine Learning approaches results showed that reducing PM 2.5 by about 4.5 times and NMVOC by about 30% would be reduced CVDALY by about 39.6% from the current status of Türkiye. The empirical results showed that PM 2.5 - NMVOC increased CVDALY in Tür-kiye. From this perspective establishing and implementing policies to improve air quality in Türkiye could be an important approach in reducing cardiovascular diseases.