<p>The increasing spread of diseases transmitted by mosquitoes, including malaria and dengue, poses a major global health challenge. Traditional mosquito detection methods, which are based on manual trapping and counting, are time-consuming and inefficient for continuous monitoring. Recently, sensor-based systems have been developed that utilize acoustic signatures. Still, their effectiveness is limited by deep learning models that struggle with noisy environments and fail to adapt to new conditions. Additionally, the scarcity of labeled data for training these models remains a significant obstacle, further reducing their accuracy and generalizability. This paper proposes a novel approach to overcome these limitations by developing an adaptable pipeline to create environment-specific deep-learning models for mosquito detection at diverse locations. This study addresses the challenge of data scarcity and evaluates various feature extraction strategies, such as log-mel and per-channel energy normalization (PCEN), can enhance model robustness in different environmental settings. Our proposed solution successfully creates models that achieve accuracy greater than 90% for any given environment, improving adaptability and supporting public health efforts to control vector-borne diseases. Experimental results confirm this by testing CNN and TCN models in different environments. PCEN preprocessing outperformed log Mel, with the CNN model achieving the highest accuracy of 93.25% in the open environment. Cross-testing results further justify the approach of using environment-specific models.</p>