Background Information: Blockchain-augmented edge computing systems enable secure and real-time data processing. However, within decentralized-varying resource and network conditions environments, such adaptive task offloading remains a challenge. Objectives: This investigation aims to implement a hybrid adaptive task offloading framework empowered through neural networks and evolutionary algorithms, which decides in an optimized fashion for edge systems-enabled blockchain technology, concentrating on latency and energy consumption mitigation Methods: Our approach uses neural networks with an evolutionary algorithm to optimize task offloading decisions in a decentralized scenario, addressing the resource management, network bandwidth, and scalability challenges. Methods Results: The proposed framework outshines traditional methods by achieving superior task offloading efficiency reduction in latency and energy consumption in blockchain-assisted edge computing environments while also demonstrating optimal scalability and maximized level of real-time execution. Conclusion: The hybrid approach makes efficient use of blockchain technology by improving task offloading in edge systems while ensuring more resource allocation, less latency, and energy usage, thereby promoting decentralized smart edge computing.