AI-based mental health chatbots demonstrate significant potential to address global treatment gaps, yet their comparative effectiveness and ethical implementation remain understudied. Through PRISMA-guided analysis of 10 solutions including Woebot, Wysa, and LLM-powered tools, this study reveals CBT-focused chatbots achieve 34–42% symptom reduction (PHQ-9) while generative AI models attain 89% emotional intent accuracy. Critical disparities emerge in cross-cultural efficacy (18% performance gaps) and long-term validation, with only 30% of studies extending beyond 6 months. The research introduces the MHealth-EVAL framework for safety assessment, demonstrating 94% harmful intent detection alongside 40% engagement improvements over conventional apps. Practical implementation guidelines emphasize three pillars: hybrid clinician-AI care models, culturally-adaptive NLP architectures co-designed with local communities, and dynamic regulatory certification processes. These evidence-based strategies position chatbots as scalable complements to traditional care when integrated with continuous outcome monitoring and transparent AI auditing protocols.