In real-time strategy games, players often face uncertainty regarding which strategy will lead to victory. This paper delves into how multi-armed bandit (MAB) algorithms can assist in this context, beginning with an exploration of MAB's theoretical principles, particularly the crucial balance between exploration and exploitation. The study compares the efficacy of the Explore-Then-Commit (ETC), Upper Confidence Bound (UCB), and Thompson Sampling (TS) algorithms through practical experimentation. Beyond gaming, the paper also considers the broader implications of MAB algorithms in healthcare, finance, and dynamic pricing within online retail sectors. A focal point of the research is the application of UCB and TS algorithms in StarCraft, a popular real-time strategy game. The performance of these algorithms is rigorously evaluated by calculating the cumulative regret value, a key metric in assessing strategic effectiveness. The findings suggest that the implementation of UCB and TS algorithms significantly enhances players' winning rates in the game. While the results are promising, the paper acknowledges ongoing challenges and encourages further exploration into this fascinating and valuable area of study. This research not only contributes to the understanding of strategic decision-making in gaming but also signals potential cross-sectoral applications of MAB algorithms.