The rapid proliferation of wearable biosensor technologies has transformed approaches to real-time health monitoring, yet challenges persist in achieving both mechanical robustness and reliable, continuous data analytics in dynamic environments. Conventional polymer-based sensing systems often fall short due to limited signal fidelity, inadequate adaptive analytics, or insufficient integration with secure, low-latency IoT frameworks. Addressing these deficiencies, this work introduces a flexible, deep learning-enhanced wearable biosensor platform that combines a nanostructured polymer composite sensor array, embedded hybrid CNN-LSTM analytics, and seamless IoT connectivity. The system is designed to autonomously capture and classify physiological events in real time, leveraging advanced signal conditioning and on-device neural inference for robust artifact rejection and precise event detection. A modular wireless interface supports both Bluetooth Low Energy and Wi-Fi transmission, enabling continuous, secure data flow to mobile and cloud endpoints. Experimental validation demonstrates that the proposed device sustains over 1,000 cycles of mechanical deformation with less than 3% resistance drift, while achieving a biosignal classification accuracy of 98.3% and average inference latency of 134 milliseconds on embedded hardware. Streaming trials show stable packet delivery with packet loss maintained below 1% across extended operation. By uniting advanced polymer engineering with explainable AI and resilient IoT design, this platform establishes a new standard for continuous, high-fidelity health monitoring in wearable formats, with significant implications for personalized medicine and smart healthcare infrastructure