In recent years, artificial intelligence (AI) has made significant progress in generating music using architectures such as RNNs, Transformers, GANs, VAEs, diffusion models, and large language models. Although these models are capable of generating structurally coherent and stylistically accurate music, they tend to lack the subtle emotional nuance and depth of human music. This paper examines the idea of emotional nuance—the ability of AI-generated music to express subtle variations, mixed effects, changing affective trajectories, and selective emotional impact. Combining theories from music psychology, affective computing, and computational creativity, I translate musical features like tempo, mode, harmony, dynamics, articulation, timbre, and melodic contour into their perceived emotional counterparts. I survey and compare methods of emotional control, ranging from conditional generation and reinforcement learning with affective rewards to employing music theory and hybrid symbolic–neural methods. I present key challenges, including the subjective nature of emotional perception, limitations in datasets, cultural variability, and the challenge of quantifying nuanced affect. I also outline directions for future work around more robust datasets, culturally adaptive models, cognitively inspired emotion representations, interpretable control mechanisms, and sound evaluation frameworks. By refining these strategies, AI music systems can move closer to being not just pattern generators but creative collaborators able to express genuine emotion.