Coronavirus has been identified as one of the deadliest diseases and WHO has declared it as pandemic and global health crisis. It has become a massive challenge for humanity. India is also being facing its fierceness as it is highly infectious and mutating at a rapid rate. Many interventions have been applied in India since the first reported cases i.e. on January 30, 2020. Several studies have been conducted to assess the impact of climatic and weather conditions on its spread in the last year span. As it is a well-established fact that temperature and humidity could trigger the onset of diseases such as influenza and respiratory disorders, the association of several meteorological variables has been studied in the past with the COVID-19 related number of cases. The conclusions in those studies were based on the data obtained at the early stage and it was too early to draw any inference. This study attempted to assess the influence of temperature, humidity, wind speed, dew point, previous day’s number of deaths, and government intervention’s effect on the number of COVID-19 confirmed cases in 18 districts of India. It is also attempted to identify the important predictors of number of confirmed COVID-19 cases in those districts. The random forest model and the hybrid model obtained by modelling the random forest model's residuals are used to predict the response variable. It is observed that meteorological variables are useful only to some extent that too when used with the data on number of the previous day’s deaths and lockdown information in predicting the COVID-19 cases. Partial lockdown is more important than the complete or no lockdown in predicting the number of confirmed COVID-19 cases. The information is useful to policy makers in balancing the restriction activities and economic losses to individuals and the government.