Los Angeles has been my home since 2000. Living in the LA/OC vicinity areas, I had to see and face the devastation of several wildfires in 25 years. The biggest ones I’ve seen were the 2020 Yorba Linda fire and now the 2025 Altadena and Palisades fires. One of my friends lost everything in that fire.
Being a tech entrepreneur all my life, I started to think about what has been done to make use of the latest technology, especially AI. In this article, I want to showcase how AI is being used or could be used to protect against wildfires.
What’s Working Today?
Let’s take a look at a few of the tech solutions being used to combat wildfires today.
- Advanced Predictive Analytics
As we already know, AI models can use large datasets, including historical fire records, real-time weather data and satellite imagery, to forecast potential wildfire outbreaks. The University of California San Diego has developed models that use machine learning models with satellite data to accurately predict wildfire spread, enhancing the ability to allocate resources effectively. - Real-Time Detection Systems
For real-time detection, AI-powered cameras and sensors can be deployed for the immediate identification of fire ignitions. The ALERTCalifornia project utilizes a network of over 1,140 AI-enabled cameras across the state, providing early detection capabilities that are crucial for prompt response. - Autonomous Aerial Surveillance
Unmanned aerial vehicles (UAVs), commonly known as drones, use AI technology and provide real-time monitoring and data collection over vast terrains. These drones can autonomously navigate through smoke and challenging environments, delivering high-resolution images and thermal data to alert commanders. - AI-Enhanced Resource Allocation
Machine learning algorithms assess variables such as terrain, vegetation and weather conditions to optimize the deployment of firefighting resources. This data-driven approach ensures that personnel and equipment are strategically positioned, improving response efficiency and effectiveness.
What Could Work In The Future
Beyond those current solutions, based on my experience in the industry, I believe there are many opportunities to innovate even further. Let’s take a look at a few possible areas for progress in this field.
- Fire Spread Models
Most wildfire predictions today rely on models already built with static data. But fires don’t follow that. They change with shifting wind patterns, human activities and climate changes.
We can use reinforcement learning (RL) for the system to learn from real-time fire behavior. These agentic AI agents can simulate multiple fire scenarios based on live satellite images and drone feeds. They can make use of fire suppression strategies in real time, helping fire departments deploy resources more efficiently. This can also help in unpredictable fire movements by continuously updating fire growth models based on current conditions. Google’s wildfire forecasting AI has improved fire growth prediction accuracy. - AI-Powered Edge Computing
Because wildfires evolve really fast, relying on drones and other sensory data delays analysis. A solution could be AI-powered edge computing.
Small AI models can be created and deployed on devices, drones and firefighter gear to process data without needing a connection to cloud servers. We could also implement 5G-enabled AI nodes to create an intelligence network where fire trucks, drones and command centers continuously communicate, even in remote regions. - AI-Generated Synthetic Data For Training
Generating simulated data is one of the best ways I believe we can handle what-if scenarios for which we do not have historical data.
Generative AI models can create synthetic wildfire simulations, replicating extreme fire conditions that firefighters have never encountered before. Virtual reality (VR) with AI can create interactive, AI-driven emergency drills. This helps prepare teams for the worst possible conditions and lets them test their capacity for dealing with those conditions. AI can also personalize training by adapting simulations based on firefighter performance for targeted improvement.
- Smarter AI For Resource Deployment
We all know that every second matters in wildfire response time. But deploying firefighters, helicopters and water tankers is often based on human decisions. AI-driven logistics optimization using predictive analytics models could assess terrain, weather, fuel conditions and firefighter availability to deploy the right resources to the right locations.
AI-powered drones could provide aerial fire mapping, identifying where fire suppression is most urgent. Real-time firefighter tracking can ensure teams don’t get trapped by shifting fire fronts.
- AI For Predictive Evacuation
One of the biggest challenges I’ve seen during wildfires is last-minute evacuations. This leads to residents panicking and sometimes getting stuck in traffic or cut off from escape routes.
AI-driven real-time evacuation routing could use satellite and drone data to redirect traffic away from danger zones dynamically. Geospatial AI models could predict where fires will spread hours in advance, allowing emergency teams to issue earlier evacuation orders. AI-powered SMS can provide personalized escape routes based on live fire data.
- AI-Managed Emergency Communication
One of the biggest risks involves cell towers going down, which can cut off firefighter communication.
AI can manage resilient, self-healing communication networks in disaster zones. Drones equipped with AI-managed LTE/5G relay stations can restore connectivity to disconnected regions. AI can predict which areas will lose network coverage next, sending temporary mobile towers before infrastructure collapses. AI-driven speech-to-text assistants can help emergency operators process calls faster—prioritizing the most urgent cases.
What’s Next?
I firmly believe AI is the future of disaster management. But for this technology to reach its full potential, we must step up. Organizations should do the following to be a part of the solution:
- Invest in AI-driven wildfire research and partner with first responders to refine predictive models.
- Develop scalable AI solutions like edge computing, federated learning and real-time analytics and vie for enterprise-scale deployment of these technologies.
- Push for AI-driven disaster policies and advocate for AI-powered emergency systems at state and federal levels.
The wildfire crisis is growing, but so is AI’s potential to save lives, protect communities and reshape emergency response as we know it. Now is the time for AI professionals to take action. Will you be part of the solution?
