AI music generation platforms now span high-capability research models and consumer-facing studios that turn text or voice prompts into finished, royalty‑free tracks. This topic covers core model classes (for example, large audio-native systems such as Google Lyria 3 and Opus‑class architectures) and the growing ecosystem of tools that sit on top of them. Recent technical trends include diffusion-based generation conditioned by lightweight transformers (used in models like ACE‑Step) to produce coherent, full-length songs in seconds, plus improved codec-aware architectures that prioritize fidelity and low-latency output. Why this matters in early 2026: advances in model quality and inference speed are making AI-generated music practical for independent creators, game and app developers, marketers, and podcasters. The space is also shifting from proof‑of‑concept syntheses toward integrated workflows that include topline MIDI, curated sound libraries, adaptive background music, automated mastering, and repurposing tools. Representative platforms include ACE‑Step (fast, coherent full-song generation and an open‑source foundation model), Evoke Music / Amadeus Code (FUJIYAMA AI SOUND, curated libraries and topline generation), Musci (studio-focused text‑to‑music and multi-tool production), Flowfi (adaptive lo‑fi soundscapes for focus), MasteringBOX (AI mastering service), and EchoPod (automated production of podcast episodes from text). Understanding these tools means evaluating generation quality, latency, control over stems/MIDI, licensing/royalty terms, and how well platforms integrate post‑production. For creators and product teams, the current landscape favors modular stacks where fast generation, accessible editing, and automated mastering are combined into end‑to‑end music workflows.