Non-Playable Characters (NPCs) in video games often exhibit predictable behaviors, leading to a potential break-in player immersion. This research investigates the revolutionary impact of Emotional Behavior Trees (EmoBT) on non-playable characters (NPCs). Departing from conventional behavior trees that often render NPCs as predictable and robotic entities, EmoBT injects emotions into their decision-making processes, seeking to cultivate lifelike and organic in-game characters. This work introduces Emotional Behavior Trees (EmoBT) as an innovative paradigm shift to address this issue. While existing work has proposed EmoBT, it falls short by neglecting crucial aspects such as sensory data, spatial awareness of NPCs, and an understanding of the player's status and position. In contrast, our approach overcomes these limitations by not only focusing on the emotional states of NPCs but also incorporating the conditions and positions of players in the game. Through a unique priority selector and consideration of factors such as time-discounting, risk perception, and strategic planning, EmoBT provides game developers with practical insights to seamlessly integrate emotions into NPC behaviors. Our comprehensive evaluation, encompassing depth, breadth, conditions tests, and accuracy analysis, demonstrates the superior performance of our system over traditional approaches, even as we acknowledge a computational time trade-off.