Integrating Predictive Analytics and Community Engagement in Kenya

This study examines how advanced technologies, including predictive analytics and community engagement, enhance wildlife conservation in Kenya’s private conservancies. Through a mixed-methods approach integrating supervised machine learning analysis of GPS ranger tracks, policy and academic document reviews, and camera trap data from Solio Conservancy, the research evaluates the efficacy of a technology-integrated conservation framework (TICF). Quantitative results demonstrate that predictive analytics achieved 89% accuracy in forecasting poaching hotspots, enabling proactive resource deployment. Qualitative findings reveal robust community support, underpinned by transparent processes, equitable participation, and strengthened trust in conservation initiatives. The TICF framework bridges technological innovation with human-centred strategies, emphasising adaptive tools and sustainable data practices to address ecological and governance complexities. Key challenges, such as data privacy risks, connectivity constraints, and long-term system maintenance, are critically analysed to inform scalable implementation. The study proposes an expansion strategy to adapt the TICF across diverse ecological contexts, offering insights into global biodiversity policy. By synergising evidence-based technologies with community empowerment, the framework positions local stakeholders as pivotal actors in wildlife protection while advancing scalable solutions for habitat preservation. This research underscores the transformative potential of integrated socio-technological systems in conservation, advocating for ethical engagement, ecological adaptability, and inclusive governance. The findings contribute to academic and policy discourses on balancing technological innovation with socio-environmental equity, highlighting pathways to achieve sustainable conservation outcomes in Kenya and beyond.