The Sundarbans community relies on mangrove forests for wild honey but is faced with remoteness, lack of filtering options and risky collection. The region is also home to Royal Bengal tigers and estuarine crocodiles. To battle these risks, sustainable beekeeping and beehive monitoring needs to be embraced. Methods: This study introduces a multi sensor system that uses Raspberry Pi for collecting and processing of beehive data to monitor beehive conditions in real-time. A variety of sensors, such as those for humidity, air quality, sound, air flow and pressure, are integrated into this system and connected to a Raspberry Pi, in contrast to conventional monitoring techniques that frequently rely on single-sensor systems with few environmental inputs. Result: This setup makes it possible to continuously check on the hive using IoT. With this setup, hive health can be continuously monitored and possible dangers, such low air quality or odd sound patterns, can be identified early. Bee hive sound data was analyzed using CNN on Edge Impulse and 96% accuracy was achieved. Conclusion: By moving the processed data to the Blynk Cloud, the ESP32 microcontroller expands the capability. The system provides a strong answer for contemporary beekeeping by supporting sophisticated data analysis using machine learning, improving forecast accuracy, and lowering false alarms through its local processing capacity