Introduction: Since COVID-19 has been characterized as a worldwide epidemic, multiple studies have suggested that weather may have a role in virus transmission. This research aims to examine the correlation between meteorological parameters and SARS-CoV-2 transmission, as well as to forecast cumulative COVID-19 cases in Bangladesh. Methods: In this study, an average incubation period of 5-6 days was used to examine the real effect of environmental parameters on SARS-CoV-2 transmission. Therefore, considering the incubation period and reporting time a standard 7-day shift in meteorological parameters from the daily COVID-19 cases was applied to measure the actual correlation. In this regard, the non-parametric correlation test (Spearman's Rank Correlation) was performed where 95% (p < 0.05) and 99% (p < 0.01) confidence intervals were considered as an acceptance criterion. Results: This work found a significant positive correlation (p < 0.01) for COVID-19 cases with minimum temperature, average temperature (only for division-specific analysis), wind speed, rainfall, humidity, and cloud. Furthermore, a significant negative correlation (p < 0.01) was found with atmospheric pressure and sun hours. However, the impact of maximum temperature (except for some divisions) or UV index was significantly low. Discussion: Our findings showed that the strength of the correlation coefficient is higher for the test positivity rate rather than the confirmed case count. However, to forecast the cumulative cases of COVID-19, ARIMA (Autoregressive Integrated Moving Average) may be considered the best-fitting model according to AIC (Akaike Information Criterion) and performs slightly better than Holt's exponential smoothing model. Additionally, this study represents the comparative analysis between predicted and actual Coronavirus-19 cases during December 2021 to show how close the predicted result is to the selected model.