This study investigates the critical techno-functional requirements for successfully integrating a predictive depression detection model into HIV care systems in Bungoma and Busia counties, Kenya, emphasizing that an accurate algorithm alone is insufficient without alignment with clinical workflows, user needs, and existing infrastructure. Through a mixed-methods approach involving 119 stakeholders, the research identified key barriers to routine screening, such as stigma (50.4%), time pressure (44.5%), and weak referral systems (43.7%), while also revealing stakeholder priorities for a user-friendly interface (62.2%), real-time alerts (58.8%), and robust privacy and security measures (72.3%). The findings advocate for a clinician-supervised architecture that integrates seamlessly with KenyaEMR and AMRS via secure APIs, providing automated risk scoring at triage with explainable results to prompt confirmatory PHQ-9 assessments. Ultimately, the study concludes that stakeholder-informed workflow design, coupled with resilient infrastructure and strong governance, is essential for translating predictive accuracy into a safe, usable, and sustainable depression screening tool within resource-constrained HIV care settings.