Breast cancer (BC) is the most frequently diagnosed malignancy in women and a leading cause of cancer-related mortality worldwide. Current clinical management relies on molecular classification—based on estrogen receptor (ER), progesterone receptor (PR), HER2, and Ki67 expression—to guide prognosis and therapy. Triple-negative breast cancer (TNBC), which lacks ER, PR, and HER2 expression, represents 15%–20% of cases and is characterized by aggressive behavior, early recurrence, and a paucity of targeted treatment options. These challenges underscore the urgent need for improved preclinical models that better recapitulate tumor biology to accelerate therapeutic discovery. While conventional monolayer (2D) cultures have contributed significantly to cancer research, they fail to mimic critical features of the three-dimensional (3D) tumor microenvironment (TME), thereby limiting clinical translation. To address this gap, 3D spheroid models have emerged as a powerful intermediary, more accurately replicating in vivo conditions such as cell–cell and cell–matrix interactions, nutrient and oxygen gradients, and the development of hypoxic cores. These features make spheroids a physiologically relevant platform for studying complex processes like metastasis, drug resistance, and treatment response. Here, we present a robust, simple, and cost-effective protocol for generating uniform 3D spheroids. Our method enables consistent monitoring of spheroid formation and growth over time, with quantitative, image-based size analysis to ensure reproducibility and scalability. Designed for flexibility, the protocol is broadly applicable across diverse cell types, effectively bridging the gap between traditional 2D cultures and complex in vivo studies. By providing an accessible and reliable model of the 3D TME, this protocol opens new avenues for high-throughput drug screening, mechanistic studies of tumor progression, and the advancement of personalized medicine strategies in breast cancer and beyond.