Overview
Machine learning forms the operational backbone of the UAE’s broader artificial intelligence ambitions. From predictive maintenance in oil and gas to fraud detection in banking, ML models are being deployed at scale across the Emirates. The nation’s investment in compute infrastructure, combined with progressive data-sharing policies, has created a fertile environment for both research and commercial ML applications.
UAE Landscape
The Technology Innovation Institute (TII) drives foundational ML research, having produced the Falcon family of large language models. MBZUAI graduates over 100 ML researchers annually, feeding a growing domestic talent pipeline. On the commercial side, organisations such as Presight AI apply ML to government analytics, while Bayanat uses geospatial ML for defence and urban planning. Abu Dhabi’s Core42 provides the GPU-dense compute fabric that powers enterprise-grade ML workloads, including the Condor Galaxy supercomputing partnership with Cerebras. Dubai’s DIFC Innovation Hub hosts over 40 ML-focused startups spanning financial modelling, healthcare diagnostics, and supply chain optimisation.
Key Players & Initiatives
| Entity | ML Application | Sector |
|---|---|---|
| TII / Falcon LLM | Foundation models & NLP | Research |
| Presight AI | Predictive government analytics | Public sector |
| Core42 | ML compute infrastructure | Cloud & HPC |
| Bayanat | Geospatial intelligence | Defence & planning |
| Injazat | Enterprise ML platforms | IT services |
| AIQ (ADNOC-G42 JV) | Upstream energy ML | Oil & gas |
| Careem (Uber) | Demand prediction & routing | Mobility |
Policy Framework
The UAE’s National Data Strategy mandates structured data collection across government entities, providing high-quality training data for ML models. Federal agencies are required to evaluate ML-based solutions before procuring traditional software. The Smart Dubai Data Law governs data access and sharing, while sector-specific regulators such as the Central Bank and MOHAP issue guidelines for ML deployment in finance and healthcare respectively. The AI Ethics Board reviews high-impact ML deployments for bias and fairness.
Vision 2031 Alignment
Machine learning directly supports Vision 2031 targets for operational efficiency, service personalisation, and evidence-based policymaking. The government aims for ML-driven automation to reduce processing times by 70 percent across federal services. Private-sector ML adoption is incentivised through R&D tax credits and co-investment programmes via Mubadala and ADQ. By 2031, the UAE targets a top-three ranking in the Global AI Index’s machine learning sub-category.