The rapid expansion of the Decentralized Finance (DeFi) ecosystem and the cryptocurrency market has fundamentally challenged the traditional structure of the financial system. Features such as decentralization, smart contracts, algorithmic liquidity, hidden leverage, and complex interconnections among protocols have led to the emergence of new forms of systemic risks that conventional risk management tools are often unable to adequately identify and predict. The objective of this study is to propose an integrated framework for assessing systemic risks arising from DeFi and cryptocurrencies by leveraging Quantum Monte Carlo simulation models and Artificial Intelligence (AI) algorithms.Within the proposed framework, the decentralized financial ecosystem is modeled as a complex adaptive system, where nonlinear dependencies among protocols, decentralized exchanges (DEXs), stablecoins, and market participants are extracted using deep neural networks and graph learning models. Subsequently, Quantum Monte Carlo algorithms are employed to simulate a wide range of scenarios involving market shocks, liquidity collapses, and risk contagion, enabling the estimation of the probability of systemic crises.The theoretical findings suggest that combining the predictive capabilities of Artificial Intelligence with the superior convergence speed of quantum algorithms can significantly improve the accuracy of systemic risk assessment compared to traditional classical approaches. Furthermore, the proposed framework provides a scalable and forward-looking methodology for monitoring financial stability within rapidly evolving digital asset ecosystems.