The rapid proliferation of the Internet of Things (IoT) has heightened the urgency to address security vulnerabilities inherent in resource-constrained connected devices. Protecting sensitive user information underscores the necessity of securing personal data. Unauthorized access threats require advanced encryption solutions to enhance security and optimize system performance. This study proposes a novel hybrid encryption framework that uniquely combines asymmetric Elliptic Curve Cryptography (ECC) with a symmetric Blowfish algorithm. Unlike conventional methods such as RSA, AES, or standalone ECC, our approach integrates these techniques innovatively to achieve superior efficiency and security. Furthermore, a new adaptive encryption protocol is developed, optimized for data volume and device capabilities. Blowfish efficiently encrypts large data (over 64 KB) while maintaining speed. Additionally, ECC employs smaller key sizes, enhancing security without significant processing overhead. This research used data from various IoT devices like sensors, wearable gadgets, smart home systems, traffic, and industrial tools, tested both in labs and real-world settings. The data included various types of sensitive information, with each dataset averaging over 50 KB. Furthermore, all experiments were implemented on a system with a dual-core Intel processor at 2.5 GHz, ensuring a consistent performance evaluation. Compared to traditional cryptographic solutions such as RSA, AES, and standalone ECC implementations, our approach demonstrates a significant improvement in execution time—greater than 15%—and substantially enhances overall efficiency. This research shows that our hybrid approach improves IoT security while saving processing power and energy, helping future device performance.