Dengue fever is a worldwide health issue caused by the virus. Effective medical management and avoidance of severe consequences need fast and precise Dengue detection. This research uses advanced image pre-processing, deep learning methods using convolutional neural networks (CNNs), and feature selection to improve Dengue diagnosis. Our first study focuses on improving input images using cutting-edge image enhancement methods, including Adaptive Contrast Enhancement using Histogram Equalization (ACE-HE)during pre-processing. This improves visual data for processing, enabling Dengue identification. Feature extraction, using a CNN architecture optimized for Dengue detection, is our core technique. We also use current neural network designs like EfficientNet and transformers to extract subtle characteristics needed for effective diagnosis. Our approach uses computational evolutionary algorithms and neural structure searches to identify the most relevant features from the large pool of retrieved information. These characteristics are gradually fused to provide a complete depiction of Dengue's complicated patterns. Dengue detection is completed using high-performance classifiers including Random Forest variations and ensemble approaches. Our architecture is adaptable and resilient, achieving above 97.92% BCCD dataset classification accuracy. This technology represents a major advance in Dengue diagnosis and meets the worldwide requirement for rapid and accurate infectious illness identification. Our strategy uses the latest computer vision and neural network technology to produce dependable and effective tools for rapid and accurate Dengue diagnosis, addressing a crucial worldwide healthcare issue.