The SaaS sector in India, poised to exceed $26.4 billion by 2029, presents a dynamic yet fragmented growth environment. This study explores the financial strategies, growth patterns, and performance drivers of Indian SaaS companies using machine-learning-based predictive modelling. A supervised model was built using SHAP value analysis across LTV/CAC ratio categories, revealing the factors that influence long-term profitability. Key findings showed that Customer Lifetime Value (CLV) was the most stable and powerful predictor of LTV/CAC, followed by lead-to-customer conversion rate and website conversion performance. These drivers consistently push profitability upward across multiple data slices. Additionally, monthly web traffic and B2B business models gain importance as companies scale. Conversely, features such as excessive ad impressions, frequent brand mentions, and high CPL (in early stages) negatively impact LTV/CAC. However, these same factors shift positively at higher efficiency levels, indicating the role of maturity and brand targeting precision. The study uses primary data from 301 SaaS professionals and supplemented it with structured literature using PRISMA. The Extra Trees Regressor achieved the best ROC-AUC score (0.85), while SHAP plots offered visual interpretability of key influencer shifts across LTV/CAC thresholds (0.5 to 4.5). These insights support a more nuanced, data-backed GTM strategy for SaaS companies—focusing on retention, efficiency, and scalable acquisition. Future research may deepen the analysis with larger, public datasets and explore the effect of external variables like regulation and economic volatility.