Depression, a complex and pervasive mental health disorder, affects millions globally and demands personalized, adaptive treatment approaches. Recent advancements in artificial intelligence, particularly the use of Large Language Models (LLMs) and Reinforcement Learning (RL), offer transformative opportunities for digital mental health support. This survey explores the intersection of LLMs and RL in developing personalized depression management systems. LLMs, such as GPT-based models, excel at generating human-like, empathetic dialogue, enabling conversational agents to simulate therapeutic interactions. These models can respond to user sentiment, detect linguistic patterns related to depression, and deliver content aligned with cognitive behavioral therapy (CBT) principles. Meanwhile, RL algorithms focus on optimizing long-term therapeutic outcomes by learning from user interactions and adapting strategies based on feedback and reward mechanisms. They enable adaptive scheduling of interventions, behavioral nudging, and habit reinforcement. This paper reviews recent advancements, architectures, clinical applications, challenges, and comparative benefits of both technologies in the context of mental health. We highlighted existing systems, emerging hybrid models, and open research questions related to privacy, personalization, and ethical deployment. The survey concludes by outlining the potential of integrated LLM-RL frameworks to revolutionize depression care through scalable, dynamic, and emotionally intelligent support systems tailored to individual needs.