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Traditional programming interpreters are primarily designed to perform lexical analysis, syntax analysis, semantic processing, and program execution according to predefined programming-language rules. Although such interpreters provide reliable and deterministic execution, they generally have limited capabilities for contextual code understanding, adaptive optimization, intelligent assistance, and continuous security analysis. Recent advances in artificial intelligence (AI), large language models (LLMs), machine learning (ML), and cloud computing provide an opportunity to transform conventional interpreters into intelligent execution environments. However, integrating AI directly into an interpreter introduces additional concerns related to malicious code execution, vulnerable AI-generated code, prompt injection, data exposure, model manipulation, privacy, and computational overhead. This paper proposes an AI-Powered Secure Intelligent Interpreter (AISII) that integrates conventional interpretation mechanisms with AI-based code understanding, vulnerability analysis, runtime behavioral monitoring, anomaly detection, secure sandboxing, adaptive policy enforcement, and cloud-assisted intelligence. The proposed architecture follows a modular, defense-in-depth approach in which source code is analyzed through lexical, syntactic, semantic, static-security, and AI-based assessment stages before execution. A runtime monitoring layer continuously evaluates execution behavior and dynamically updates a risk score. Depending on the estimated risk, the security policy engine can permit, restrict, isolate, or terminate execution. The framework further incorporates local, edge, and cloud intelligence to balance privacy, latency, and computational scalability. A comparative analysis positions the proposed architecture against conventional interpreters, AI-assisted programming tools, static analysis systems, and autonomous software-engineering agents. An experimental methodology is also proposed using code-generation benchmarks, vulnerability datasets, benign/malicious execution workloads, and real-world software-engineering tasks. Evaluation metrics include execution latency, resource utilization, vulnerability-detection precision, recall, F1-score, false-positive rate, and containment effectiveness. The paper concludes with an implementation roadmap and discusses federated learning, edge AI, autonomous programming, and quantum computing as future research directions. The proposed framework provides a foundation for developing interpreters that are not merely execution engines but adaptive, intelligent, and security-aware computing environments.