The rapid proliferation of Large Language Models (LLMs) specialized in code generation has introduced a transformative yet volatile era in software development. While these models facilitate unprecedented productivity, they often produce code that adheres to functional requirements while simultaneously violating architectural best practices, maintainability standards, and security protocols. This research article provides a comprehensive investigation into the current state of automated code review and neural code generation, synthesizing empirical evidence from polyglot benchmarking platforms and specialized evaluation frameworks. By integrating classical metrics, such as cyclomatic and cognitive complexity, with modern deep learning-based review automation like LLaMA-reviewer and ChatGPT-driven refinement, this study explores the efficacy of real-time feedback systems in ensuring secure and maintainable software development. The analysis delves into the theoretical foundations of software experimentation, the limitations of static analysis in detecting latent bugs, and the emerging potential of parameter-efficient fine-tuning for code review automation. The findings suggest that while neural models have made significant strides in polyglot generation, a "semantic gap" remains between functional completion and long-term code health, necessitating a hybrid approach that combines generative intelligence with rigorous architectural constraints and automated refactoring principles.