This research paper examines the statistical inference on the forest fire danger index by considering the influential factors of temperature and humidity using Wireless Sensor Networks. We Design the WSN architecture and select appropriate communication protocols (MQTT) to facilitate efficient and reliable data transmission. The study aims to understand the relationship between the forest fire danger index and these environmental variables and determine their impact on fire risk assessment. To achieve this, several commonly used statistical methods are applied, including regression analysis, correlation analysis and time series analysis such as Autoregressive Integrated Moving Average (ARIMA). By utilizing these methods, the paper explores the interplay between temperature, humidity, and the fire danger index to gain insights into patterns, trends, and predictive capabilities for assessing and mitigating forest fire risks