⚠ Not for operational use — for reference and development purposes only
Flight Time Estimator
Gate-to-gate flight time predictions powered by machine learning. Enter departure, arrival, and aircraft type.
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estimated gate-to-gate
- Distance
- Aircraft
- Model
Flight time predictions via SkyLink ML API. View API docs →
ML predictions in one request
The flight time ML endpoint returns a probabilistic estimate trained on millions of real flights. Pass departure, arrival, and optionally an aircraft ICAO type code. Get back estimated minutes, confidence range, and distance.
API Reference →GET https://skylink-api.p.rapidapi.com/ml/flight-time
?from=EGLL&to=KJFK&aircraft=b77w
x-rapidapi-key: YOUR_KEY
// Response
{
"origin": "EGLL",
"destination": "KJFK",
"aircraft_type": "B77W",
"distance_nm": 3462.64,
"estimated_minutes": 437,
"estimated_hours_display": "7h 17m",
"min_minutes": 415,
"max_minutes": 460,
"model_version": "1.0.0"
}How it works
What aircraft type codes should I use?
Use 4-character ICAO type designators: B738 (737-800), A320, A321, B77W (777-300ER), B789 (787-9), A359 (A350-900), A388 (A380-800), B744 (747-400), CRJ9, E190. A full list is in ICAO Doc 8643.
Does this include taxi time?
Yes — the model estimates gate-to-gate (block time), which includes average taxi-out, airborne time, and taxi-in. This is the time you'd see on a published schedule, not just wheels-up to wheels-down.
How does it handle wind?
The ML model learns seasonal jet stream effects from historical data. Eastbound transatlantic flights are typically 45–90 minutes shorter than westbound due to prevailing westerly winds at cruise altitude. This is baked into the predictions implicitly.
Add ML flight predictions to your app
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