The researchers aimed to develop a multi-sensor automated system for mosquito detection and environmental surveillance, Mosquitect, having a purpose of offering support in the early assessment of dengue risks in areas prone to mosquito presence. MosquiTect is designed with the use of an Arduino UNO R4 Microcontroller that aids in tracking wingbeat frequencies, temperature, humidity, and visual-based detection of mosquitoes through a camera module. The data analysis is performed by utilizing percentages and means. During the 14-day testing, the findings show that MosquiTect had a 97.64% of success rate in terms of detecting wingbeat frequencies and gender identification signals; temperature and humidity provided a 100% success rate in monitoring environmental parameters; and a 77.07% success rate in terms of visual-detection of mosquitoes. The result shows that MosquiTect holds high relevance in the facilitation of preventive steps against dengue, especially in tropical areas. MosquiTect also possesses strong practicality for aiding governmental departments in forming preventive measures for dengue. This cultivates improvements in the capability of optical detection and image recognition, energy efficiency, environmental surveillance, and predictive modelling for the population of mosquitoes and their potential dengue outbreaks by the public health agencies.