This thesis explores the applications of deep learning (DL) techniques across various domains, demonstrating significant improvements in the detection, classification, and analysis of complex data. The study integrates DL models with different analytical methods to enhance performance in several fields. In the field of electrochemical analysis, a DL-based approach using an immunobiosensor was developed for the detection and classification of CD36. Traditional techniques often fall short in sensitivity and rapid analysis, especially at low analyte concentrations. The integration of DL models such as 1D-CNN and hybrid 1D-CNN – LSTM networks significantly improved the biosensor's sensitivity and specificity. For biomedical applications, Vision Transformers (ViT) techniques were employed to classify hand movements using surface electromyography (sEMG) signals. By analyzing sEMG data with advanced time-frequency analysis (TFA) methods and various ViT models, high accuracy was achieved. In optical sensing, DL techniques were applied to analyze Phase-Optical Time-Domain Reflectometry (Phase-OTDR) data. The use of DL methods, including 1D-CNN, 1D-CNN – LSTM, and 1D-CNN – Bi-LSTM models, enhanced the efficiency of Phase-OTDR-based current sensing systems. Additionally, a method to convert optical signals into images for classification using Transfer Learning models was implemented, resulting in high classification accuracy and more efficient data storage. This thesis demonstrates the potential of integrating DL techniques with various analytical methods to achieve significant advancements. The studies show DL's versatility in enhancing data analysis performance, offering more accurate, sensitive, and efficient solutions. The methodologies developed can be extended to other biomarkers, signal types, and analytical challenges.