Molecular communication (MC), a biologically inspired technology, enables applications in nanonetworks and the Internet of Everything (IoE), with great potential for intra-body systems such as drug delivery, health monitoring, and disease detection. This paper extends our prior work on the Flexure-FET MC receiver by integrating a competitive binding model to enhance performance in high-interference environments, where multiple molecular species coexist in the reception space. Previous studies have largely focused on ligand concentration estimation and detection, without fully addressing the effects of inter-species competition for receptor binding. Our proposed framework captures this competition, offering a more biologically accurate model for multitarget environments. By incorporating competition dynamics, the model improves understanding of MC behavior under interference. This approach enables fine-tuning of receptor responses by adjusting ligand concentrations and receptor affinities, thereby optimizing the performance of the Flexure-FET MC receiver. Comprehensive analysis shows that accounting for competitive binding is crucial for improving reliability and accuracy in complex MC systems. Factors such as signal-to-noise ratio (SNR), symbol error probability (SEP), interferer concentration, and receptor dynamics are shown to significantly affect performance. The proposed framework highlights the need to manage these factors effectively. Results demonstrate that modeling interference through competitive binding offers a realistic system perspective and allows tuning of receiver response, enabling robust detection in environments with multiple coexisting species.