This study aimed to identify the best forecasting model and evaluate if time (in months and years) is a predictor for Myocardial Infarction cases in the Philippines. The research design was quantitative research, specifically descriptive study design. Secondary data on monthly reported cases of myocardial infarction for the past five years (November 2018-November 2023) from Google Trends was utilized. Microsoft Excel and SPSS were the software used to obtain results. Simple Moving Average (SMA) and Exponential Smoothing Method (ESM) were the forecasting techniques used in this study. At the same time, Mean, Standard Deviation, Analysis of Variance, and Independent sample t-test were the statistical tools utilized for both descriptive and inferential analyses. Results revealed that the mean level of cases reported for the past five years was 54. Also, according to SMA and ESM, the forecasted cases for the next succeeding month (December 2023) were 66 and 61, respectively. Results also showed that the Mean Squared Error (MSE) value of SMA is lower than ESM, making SMA a better forecasting method. Moreover, there was a significant difference between the reported cases when grouped according to year, with Year 5 having the highest number of cases and Year 1 having the lowest. Further, there was no significant difference between the SMA and ESM forecasted cases. Furthermore, time (year and month) significantly predicted the number of myocardial infarction cases reported in the Philippines.