A method comprising: receiving, at a computer system, a plurality of drilling parameters from a drilling operation; applying, by the computer system, the plurality of drilling parameters to an ensemble prediction model comprising at least three machine-learning algorithms operated in parallel, each machine-learning algorithm predicting a probability of occurrence of a future stuck pipe event based on at least one of the plurality of drilling parameters, the ensemble prediction model creates a combined probability based on the probability of occurrence of the future stuck pipe event of each machine-learning algorithm; and providing an indication of a likelihood of a future stuck pipe event to a drilling operator, the indication based on the combined probability, wherein applying the plurality of drilling parameters to the ensemble prediction model further comprises: applying at least a portion of the plurality of drilling parameters to a first machine-learning algorithm, the first machine-learning algorithm predicting a first probability of occurrence of the future stuck pipe event; applying at least a portion of the plurality of drilling parameters to a second machine-learning algorithm distinct from the first machine-learning algorithm, the second machine-learning algorithm predicting a second probability of occurrence of the future stuck pipe event; applying at least a portion of the plurality of drilling parameters to a third machine-learning algorithm distinct from the first and second machine-learning algorithms, the third machine-learning algorithm predicting a third probability of occurrence of the future stuck pipe event; applying at least a portion of the plurality of drilling parameters to a fourth machine-learning algorithm distinct from the first, second and third machine-learning algorithms, the fourth machine-learning algorithm predicting a fourth probability of occurrence of the future stock pipe event; and combining, by the computer system, the first, second, third and fourth probabilities, the combining creating the combined probability, wherein the first to fourth machine-learning algorithms respectively are a neural network, a decision tree, a support vector machine and Bayesian methods. receiving, at a computer system, a plurality of drilling parameters from a drilling operation; applying, by the computer system, the plurality of drilling parameters to an ensemble prediction model comprising at least three machine-learning algorithms operated in parallel, each machine-learning algorithm predicting a probability of occurrence of a future stuck pipe event based on at least one of the plurality of drilling parameters, the ensemble prediction model creates a combined probability based on the probability of occurrence of the future stuck pipe event of each machine-learning algorithm; and providing an indication of a likelihood of a future stuck pipe event to a drilling operator, the indication based on the combined probability, wherein applying the plurality of drilling parameters to the ensemble prediction model further comprises: applying at least a portion of the plurality of drilling parameters to a first machine-learning algorithm, the first machine-learning algorithm predicting a first probability of occurrence of the future stuck pipe event; applying at least a portion of the plurality of drilling parameters to a second machine-learning algorithm distinct from the first machine-learning algorithm, the second machine-learning algorithm predicting a second probability of occurrence of the future stuck pipe event; applying at least a portion of the plurality of drilling parameters to a third machine-learning algorithm distinct from the first and second machine-learning algorithms, the third machine-learning algorithm predicting a third probability of occurrence of the future stuck pipe event; applying at least a portion of the plurality of drilling parameters to a fourth machine-learning algorithm distinct from the first, second and third machine-learning algorithms, the fourth machine-learning algorithm predicting a fourth probability of occurrence of the future stock pipe event; and combining, by the computer system, the first, second, third and fourth probabilities, the combining creating the combined probability, wherein the first to fourth machine-learning algorithms respectively are a neural network, a decision tree, a support vector machine and Bayesian methods.