What Machine Learning Technique can I use to judge boxing fights?
It depends by what you mean by the result of each round. In boxing after each round fighters receive from $7-10$ points in case if none of the fighters was KO, submitted or disqualified. An extract from Wikipedia :

The 10-point Must System is the most widely used scoring system since the mid-twentieth century. It is so named because a judge &quot;must&quot; award ten points to at least one fighter each round (before deductions for fouls). Most rounds are scored 10–9, with 10 points for the fighter who won the round, and 9 points for the fighter the judge believes lost the round. If a round is judged to be even, it is scored 10-10. For each knockdown in a round, the judge deducts an additional point from the fighter knocked down, resulting in a 10–8 score if there is one knockdown or a 10–7 score if there are two knockdowns. If the referee instructs the judges to deduct a point for a foul, this deduction is applied after the preliminary computation. So, if a fighter wins a round, but is penalised for a foul, the score changes from 10–9 to 9-9. If that same fighter scored a knockdown in the round, the score would change from 10–8 in his favour to 9–8. While uncommon, if a fighter completely dominates a round but does not score a knockdown, a judge can still score that round 10–8.

Whether the points for each round given, the decision is straightforward - simply a sum of all scores for each round and doesn't require any machine learning.
However, if the question is - how to judge a particular round, the question is more involved. One may collect the scores on referee cards with the number of significant strikes, strikes landed, knockdown's, control of the ring, e.t.c, or even attempt to implement an algorithm, which given the video, detects, whether the strike has landed on the opponent, was significant. But it seems to be rather a tough task, and the data mining for supervised algorithm would be quite expensive.