The global shift towards sustainability is creating a compelling yet complex investment landscape, particularly in emerging markets (EMs). The Green Investor's Dilemma encapsulates the tension between the pursuit of environmental, social, and governance (ESG) objectives and the fulfilment of traditional financial goals return, risk, and liquidity. This dilemma is especially pronounced in EMs, where high-growth potential in green sectors is counterbalanced by political, regulatory, and market volatility. This review paper presents a novel, interdisciplinary framework to address this challenge. We synthesize insights from financial theory, neuromarketing, and advanced Artificial Intelligence (AI) and Machine Learning (ML) to propose a new paradigm for eco-investment portfolio optimization. First, we analyze the unique risk-return profile of EM eco-investments, including renewable energy, sustainable agriculture, and circular economy ventures. Second, we explore how neuromarketing using tools like fMRI and EEG can decode the implicit cognitive and emotional biases (e.g., hope, fear, trust) that influence investor decisions towards green assets, often creating a gap between intent and action. Third, and most critically, we investigate how AI and ML methods, such as reinforcement learning, natural language processing (NLP) for sentiment analysis, and complex portfolio optimization algorithms, can integrate these behavioural insights with multidimensional financial and ESG data to construct robust, adaptive portfolios. We conclude that the resolution of the Green Investor's Dilemma lies in a tripartite approach: leveraging neuromarketing to understand investor psychology, employing AI to navigate the complex data landscape of EMs, and developing new financial models that explicitly price sustainability risk, thereby aligning planetary health with portfolio performance.