Equity in affordable housing development is increasingly influenced by how effectively data-driven investment intelligence is communicated to diverse stakeholders. This study examines how artificial intelligence (AI) and strategic communication can be synergistically employed to address market inefficiencies, align investor objectives with housing equity goals, and ultimately drive inclusive urban development. While the affordable housing sector generates vast quantities of structured and unstructured data—ranging from demographic trends and land use to financial risk projections—investors often encounter difficulty in interpreting these insights due to inconsistent narrative frameworks and opaque reporting. To bridge this divide, the study introduces a multidisciplinary approach combining AI-powered real estate analytics with behavioral segmentation and targeted messaging. It outlines how machine learning models can forecast housing demand, price sensitivity, and risk exposure, while communication strategies can distill these insights into investor-relevant formats such as narrative dashboards, equity-centric reports, and scenario-based visualizations. Using real-world case studies, the paper illustrates how this integration not only enhances capital allocation efficiency but also improves transparency in stakeholder dialogues and project impact assessment. The research concludes by proposing a strategic framework for leveraging AI in equity-sensitive housing investment, emphasizing policy coherence, trust-building, and data ethics. It contributes to the growing body of knowledge at the intersection of proptech innovation, inclusive finance, and public-private development strategy.